Method and apparatus of neurological feedback systems to control physical objects for therapeutic and other reasons

ABSTRACT

A method and apparatus using brainwaves to control real objects is provided. The method and apparatus comprise using sensors to detect the brain&#39;s electrical signals and transmit at least two brainwaves to an apparatus that converts the brainwaves into a format usable by a signal processor. The signal processor determines a coherence between portions of the brainwaves, typically in the frequency domain, and compares the coherence values, which change rapidly from moment to moment, to thresholds. Based on the comparison of the coherence value to the thresholds, which are adjusted over time based on feedback relating to success, a control signal is developed that can be sent to a real object to control 3 dimensional motion of the control object.

CLAIM OF PRIORITY UNDER 35 U.S.C. §119

None.

CLAIM OF PRIORITY UNDER 35 U.S.C. §120

None.

REFERENCE TO CO-PENDING APPLICATIONS FOR PATENT

None.

BACKGROUND

1. Field

The technology of the present application relates generally toneurological feedback systems, and more specifically to usingneurological systems to control physical objects for therapeutic andother reasons.

2. Background

Biofeedback has been a known conditioning and therapeutic technique foryears. Generally, the treatment and training provides a sensor toidentify or monitor a physiological condition, such as electrodermal orgalvanic skin response (EDR or GSR), heart rate variability (HRV), orthe like, and a display, such as a computer or television screen. Acontrol processor receives the sensor information and, in manysimplistic devices, adjusts the displayed response to encourage anappropriate response. For example, a resting HRV may provide a balloonimage on a display floating high above a landscape. As the patient's orsubject's HRV becomes more indicative of agitation, the balloon may fallcloser to the horizon on the display.

Another type of biofeedback relates to the electrical functioning of thebrain and is generally referred to as neurofeedback. Brain functionsgenerate electrical signals (brainwaves) that may be sensed byelectrodes typically in close proximity to the skull. Controlling thebrainwaves or conditioning the brain to produce certain types ofbrainwaves is believed to facilitate wellness. In this regard,neurofeedback is of particular interest to psychologists and the like asthe brain is the central organ that controls emotions, physical actions,thoughts, and behaviors. Thus, it is believed that influencing the brainto produce particular types of brainwaves may facilitate correctivetraining for various disorders, such as, for example, anxiety,depression, attention deficit and hyperactivity disorder, Asperger'ssyndrome, obsessive compulsive disorder, and the like.

Using an electroencephalograph (EEG) in conjunction with computerprocessors allows precise and fast determination of brainwave activity.Generally, brainwaves are classified into five generic categories:

1. Brainwaves having a frequency of 2 to 4 hertz are generally referredto as Delta waves;

2. Brainwaves having a frequency of 4 to 8 hertz are generally referredto as Theta waves;

3. Brainwaves having a frequency of 8 to 12 hertz are generally referredto as Alpha waves; and

4. Brainwaves having a frequency of 12 to 26 hertz are generallyreferred to as Beta waves; and

5. Brainwaves having a frequency of 26 to 50 hertz are generallyreferred to as Gamma waves.

These brainwaves are measured by sensing the electrical signals of thebrain using electrodes. The electrical signals sensed by the electrodesare passed through band pass filters to isolate brainwaves in particularfrequency ranges to establish the Delta, Theta, Alpha, Beta and Gammawave sets. Typically, Delta waves are associated with deep sleep brainactivity. Theta waves are often associated with a transitional phasebetween sleep and wakefulness. Alpha waves are typically associated withperiods relaxation, such as, for example, meditation. Beta waves areassociated with concentration and task specific activities.

One popular way to analyze brainwaves is to measure the coherence ordiscoherence (also referred to as non-coherence and incoherence) betweenthe brainwaves from various portions of the brain in a particularfrequency range. For example, a person's Alpha waves from two differentregions of the brain can be compared. The degree of similarity betweenthe Alpha waves of the two regions would determine the degree ofcoherence.

Not uncommon today, a person undergoing neurofeedback treatment uses avideo display to provide real-time visual feedback in response todetected brainwave activity. A computer processes the brainwaves andcompares the actual brainwave to a normalized or desired pattern. The“closeness” to the normalized or desired pattern provides an input tothe processor controlling the video display. Based on the desiredactivity, the video may behave in a pattern to induce the user to alterthe user's brainwave. Thus, the visual feedback can be used to conditionthe brain to produce a desired brainwave pattern matched to a normalizedor desired brainwave pattern. Recently, a trend has existed that usesthe information to control video in simulations more akin to gameplaying than therapeutic exercises. Today's technology regarding usingbrainwaves in neurofeedback therapeutic devices and/or game playing,however, is relatively unsatisfactory. In particular, due in part to therapidity that brainwaves change, the brainwave coherence values provideat best an unstable signal to control video. Moreover, the instabilityof the signal makes it difficult or virtually impossible to control aphysical device based on brainwave activity. However, for at leastimproved therapeutic results, it would be preferable to provide a realvs. virtual visual feedback mechanism. Moreover, for both game playingand therapeutic systems, brainwaves typically change overly rapidly fora fine-tuned control, thus today's systems generally have only sluggish,course controls that are unsatisfactory for either games or therapies.

Thus, against this background, it would be desirous to provide a methodand system of using brainwaves to provide improved therapeutic andgame-playing controls.

SUMMARY

Aspects of the technology of the present application disclosed hereinaddress the above stated needs by providing a method of training apatient/user to produce desired brainwave patterns. The method oftraining comprises obtaining two or more brainwave signals from apatient/user. The brainwave signals are separated into correspondingbands and compared to determine a coherence between the brainwavesignals. The coherence between the signals is compared to apredetermined threshold to determine whether the brainwave signals arebehaving in a desired manner. Based on comparison to the threshold, acontrol signal output is provided to a control object that moves basedon the control signal. Controlling at least one control object using atleast one control signal based on the comparison, such that the controlobject moves and provides visual feedback to the patient tending tocause the user to produce desired brainwave in response to aneurological condition.

Another aspect of the technology provided in the present applicationincludes converting the brainwave signals into the frequency spectrumand filtering the brainwaves into a number of frequency bands for whicha coherence determination is desired. Each of the coherence values isused in a comparison to a threshold to develop a plurality of controlsignals to a control the physical motion of an object.

Another aspect of the technology relates to converting brainwaves intocontrol signals that can be wirelessly transmitted to a remote object.The control signals direct the motion of the remote object.

In one aspect, the technology of the present application may be appliedto video simulations, such as, gaming inputs to control virtual objects.The input directs the motion of the virtual object and/or the difficultyof the background associated with the game.

In still another aspect of the technology, the present applicationprovides mechanisms to allow brainwaves to control prosthetics such as,for example, artificial arms and legs. The technology may allow forindividual training to produce brainwaves that are converted toelectrical control signals to operate the prosthetics. The electricalcontrol signals may be provided wirelessly or wired.

Another aspect of the technology provides cognitive feedback to a personusing the device. The cognitive feedback relates to sensory input thatmay tend to cause the brain to produce the desired brainwave patterns.The sensory input may include visual feedback based on a video monitorand real object motion.

The technology of the present application also provides non-cognitivefeedback to a person using the device. The non-cognitive feedbackrelates to sensory and non-sensory input that may tend to cause thebrain to produce the desired brainwave patterns. The non-cognitivefeedback may include low frequency sound, rapid visual stimulation,electrical impulse, and feedback altering processing values to morereadily allow the user to achieve the desired goals.

Still another aspect of the technology provided in the presentapplication includes an apparatus to provide a binaural beat to induce apatient/user to produce brainwaves in a particular frequency range.

It is to be understood that the scope of the invention is to bedetermined by the claims as issued and not by whether a given embodimentincludes any or all features set forth in this Summary of by whether agiven embodiment addresses any or all issues identified in theBackground of this Application. There are additional aspects of varyingembodiments of the present application. These additional aspects areapparent to one of ordinary skill in the art on reading the disclosureset forth below and as set forth in the claims herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram of an exemplary embodiment of thetechnology of the present application;

FIG. 2 is an exemplary methodology using the technology of the presentapplication;

FIG. 3 is a is a functional block diagram of extracting signals from apatient in accordance with the technology of the present application;

FIG. 4 shows various modules of the control processor of FIG. 1;

FIG. 5 shows portions of a binaural beat generator of the controlprocessor of FIG. 1;

FIG. 6 shows a control module of FIG. 1; and

FIG. 7 is an exemplary embodiment of a display of information associatedwith the technology of the present application.

DETAILED DESCRIPTION

The technology of the present application will now be explained withreference to the figures. The figures which disclose various exemplaryembodiments of the technology generally relate to a method and apparatusdesigned to use brainwaves to control objects or inputs to controlsystems. In one exemplary embodiment, the apparatus and method can beused to control real objects, such as a remotely operated car, vehicle,keyboard, prosthesis, or the like. In other exemplary embodiments, theapparatus and method provide control signals to virtual objects. Instill other exemplary embodiments, the apparatus and method providecontrol signals to both real and virtual objects. In general, thetechnology of the present application, however, is described in relationto therapeutic uses. One of ordinary skill in the art, however, onreading the disclosure herein would understand the technology of thepresent application has many non-therapeutic uses, such as, for example,thought-controlled remote exploration, thought-controlled input/outputdevices for interaction between disabled people, thought-controlledinput to electro-mechanical prosthesis devices, and the like. The word“exemplary” is used herein to mean “serving as an example, instance, orillustration.” Any embodiment described herein as “exemplary” is notnecessarily to be construed as preferred or advantageous over otherembodiments. Moreover, all the embodiments described herein should beconsidered exemplary unless specifically noted otherwise.

Generally, the term telekinesis refers to the ability to influenceobjects solely through the use of the mind. The technology of thepresent application is not telekinesis, but rather uses the energy ofthe cerebrum to provide inputs to a processor. The processor processesthe inputs and provides control signals that influence the objects. Theprocess of using brainwaves as inputs to a control processor to controland object may be referred to as cerebral robotics or CEREBOTIC control.

Brainwaves are derived from any animal with brain functionality. Whileit could be used on numerous animals, the technology of the presentapplication is described with particular reference to a human. For easeof reference, the brainwaves may be obtained from a player, user,subject, patient, or the like, which are generally used interchangeablyherein.

As mentioned above, conventionally, brainwave coherence, or incoherence(also referred to as discoherence or non-coherence), is measured bycomparing a patient's brainwave coherence values calculated between tworegions of the cerebral cortex (or outer brain) against normalizedbrainwave coherence values. The technology of the present application,however, measures coherence between multiple parts of a single brain andcompares them to values associated with improvement. For example, aprocessor compares the patient's left hemisphere Alpha wave to thepatient's right hemisphere Alpha wave to determine coherence between theleft and right hemispheres of the brain. Alternatively, coherence may bemeasured between two areas of the brain that may operate cooperativelyfor a particular task, the areas may be left/right hemisphere,front-left/back-left portions of the brain or the like. Designations ofleft, right, front, and back as used herein should not be consideredlimiting for purposes of the technology of the present application, butrather as a relative location. A left hemisphere brainwave and righthemisphere brainwave, for example, could easily be considered a firstbrainwave and a second brainwave.

As mentioned above, one aspect of the technology of the presentapplication relates to using the technology to improve therapeutictraining of the brain. Thus, the present application is describedlargely in relation to therapeutic aspects of the technology. However,one of ordinary skill in the art will recognize on reading the presentdisclosure that the technology of the present application may be usedfor alternative applications, such as, for example, game input, remoteoperating equipment, prosthesis control, and the like.

Referring now to FIG. 1, a functional schematic diagram is provided thatgenerally outlines a therapeutic system 100. The therapeutic system 100includes one or more patient brainwave sensors 102 supported proximate apatient 108, a control processor 104 (which is shown as one component inFIG. 1, but as will be understood from the below may in fact be severaldistinct processors or a single integrated unit), and a control object106. Patient 108 would be provided with sensors 102 to detect electricalactivity of the patient's brain (not specifically shown). The sensors102, or electrodes, may be individually adhered to the patient 108 orprovided in a support 101, such as for example, a helmet, headband, orother head wear as a matter of design choice. For therapeutic use, itmay be beneficial to provide sensors as individual electrodes and not ina pre-developed helmet, for example, to facilitate placement around theskull as desired by the doctor, therapist, or the like.

The actual sensors 102 and placement of the sensors 102 are generallywithin the ordinary skill in the art and will not be further explainedexcept for completeness relating to the technology of the presentapplication. The sensors 102, which may include a processor 109 topreprocess the sensed electrical signals, transmit the signals tocontrol processor 104 through a communication link 110. Communicationlink 110 may be a cable, bus, ribbon cable, solder connection, wirelesstransmission, or the like. If a wireless transmission, sensors 102 wouldrequire a RF transmitter, such as, a Bluetooth transmitter, Zigbeetransmitter, or the like, and an associated antenna. Control processor104 would similarly require a corresponding RF receiver and antenna.Control processor 104 and control object 106 are connected bycommunication link 112. Similar to communication link 110, communicationlink 112 may be a cable, bus, ribbon cable, solder connection, wirelesstransmission, or the like. To facilitate control of a real object (vs. avirtual object), communication link 112 as a wireless link may providegreater potential range of motion. If control object is a virtualobject, such as a display 119, a cable connection may be sufficient. Thecommunication links 112 and 110 may sometimes be referred to as datalinks. The communication links 112 may be networked connections such as,for example, through a LAN, WAN, WLAN, WWAN, WiFi, Internet, Ethernet,personal area network or PAN, a private network, or the like.

As described above, and as shown in FIG. 1, sensors 102 are arranged topick up a left hemisphere brainwave and generate a corresponding lefthemisphere brainwave signal 116 and to pick up a right hemispherebrainwave and generate a corresponding right hemisphere brainwave signal118 (see FIG. 3 showing signals being transmitted to control processor104). The left hemisphere brainwave signal 116 and the right hemispherebrainwave signal 118 are sometimes referred to as first or secondbrainwave signal and collectively as brainwave signals. For example,sensors 102 may be arranged in various combinations, but one exemplaryplacement of sensors 102 includes three sensors at CT3, Cz, CT4 (whichis halfway between International 10-20 sites C3-T3 and C4-T4) and tworeference sensors at A1 and A2.

The individual signals are input to control processor 104. Controlprocessor 104, as will be explained in more detail below, processes thesignals to develop control output signals 118 transmitted to controlobject 106. While shown in FIG. 1 as a single control processor 104,control processor 104 may be several independent processors connectedvia a network, such as, for example, an Ethernet, a LAN, a WAN, a WLAN,WiFi, Internet, or the like. Control processor 104 controls the majorfunctions of the therapeutic system 100 including providing computingfunctionality to process the inputs (brainwaves signals) and/or datarequired for operation of the therapeutic system 100. Control processor104 may include a user interface 10 to allow a therapist or the like tointeract with the processes described below to manually adjust varioussettings, inputs, and the like. User interface may comprise a typicalinterface for such a device including keyboards, alphanumeric pads,mouse, track balls, light pens, touch screens, voice recognition,microphones, speakers, and the like. Moreover, control processor 104 mayinclude one or more memory banks 15 connected or integrated into controlprocessor 104 or the additional components associated with controlprocessor described below. Memory 15 may be volatile and/or nonvolatilememory on any suitable media. Memory 15 may include code, routines,modules, or the like to allow control processor to perform variousoperations and functionality as herein described.

Referring now to FIG. 2, an exemplary method 150 of operatingtherapeutic system 100 will be explained. While method 150 is describedas comprising discrete steps or actions in a particular order, the stepsoutlined below are provided in an exemplary order for ease ofunderstanding. The methodology outlined may be executed in more, less,and different steps arranged in the same or different orders.Additionally, the methodology described herein may be a substantiallycontinuous process where the steps are performed substantiallysimultaneously, or discretely as described.

Method 150 will be explained in context of providing therapeuticinfluence to assist an individual (patient 108) to produce brainwavepatterns to minimize the effects of a neurological disorder. However,the system could likely be used to train patient 108 to producebrainwaves to augment a particular neurological effect as well. Also,the methodology 150 could be used for other than therapeutic matters,such as, for example, game input, prosthesis control, or the like.

Referring specifically to FIG. 2, the method is initialized at step 152.Initializing the method may include the step of placing sensor 102, step152A, initiating timing and control of the processors, step 152B,initiating the control object 118, step 152C, and the like. Onceinitiated, therapeutic system 100 begins obtaining at least twobrainwave signals from at least two sections of the brain, step 154. Themethodology and system 100 described herein generally refers to a lefthemisphere brainwave signal and a right hemisphere brainwave signal asan example, but the two brainwave signals may be generated from any twoportions of the brain as desired. The raw brainwave signals are providedto a filtering system to filter the brainwaves into frequency bands,step 156. Filtering the brainwaves into frequency bands may includeconverting the brainwave signal into a format usable by a signalprocessor, step 156A, performing a Fourier transform on the brainwavesignal, step 156B, and the like. The filtering may be accomplished byany number of conventional devices, such as, for example, low pass, bandpass, and high pass filters as are commonly understood in the art.

The technology of the present application is described with relation totherapeutic uses. Thus, in accordance with conventional therapeuticmethodologies, the brainwaves are described as being filtered into anAlpha Theta range, a sensory motor rhythm range, a Beta range, and aGamma range. Once filtered into frequency bands, a coherence between thetwo brainwave signals in each band is determined, step 158. Notice,distinctly unlike conventional therapeutic methodologies that comparecoherence of a patient's brainwave to a normative database, the presentapplication compares two brainwave signals of the patient for a trend inthe coherence towards or away from a “therapeutic” value. Such atherapeutic value may include, for example, that once Alpha wavecoherences is above 75% it does not fall below 70% coherence. Forexample, the left hemisphere brainwave produces, after filtering, anAlpha Theta range signal and the right hemisphere brainwave produces,after filtering, an Alpha Theta range signal. The left and righthemisphere Alpha Theta range signals would be compared to determine acoherence between the signals. The coherence would be used to determinewhether coherence is moving towards the therapeutic goal. Conventionalmethodologies would typically compare the left, right, or a combinationof the Alpha Theta range to a normative signal in a database. Thetechnology of the present application, however, in the first instancedoes not compare any brainwave coherences to a normative database ofbrainwave coherence.

In addition to coherence, another therapeutic value that may be measuredis the amplitude or energy of a particular wave form. Thus, at step 160,the amplitude or energy of a signal is determined. The signal may betotal energy of the left hemisphere signal, total energy of the righthemisphere signal, total energy of the left and right hemisphere signal,energy of the Alpha Theta brainwaves, or any possible combinationthereof.

Using the coherence values, amplitude or energy values, or a combinationthereof, method 150 compares the values against one or more thresholdvalues, step 162. As can be appreciated, brainwave signal changes aregenerally rapid. Thus, the coherence value is often changing rapidly andis difficult to monitor, making it especially difficult to providestable control signals to control the motion of real objects. However,by comparing coherence, amplitude or energy, to a threshold value, anoutput signal based on the comparison may remain constant over anextended period. For example, Alpha Theta coherence may be trending inan upwards direction, but frequently dips into a downwards direction(i.e., incoherence or discoherence) on its trend toward more coherence.Thus, although the actual coherence value may be in flux, the coherenceabove a threshold or the coherence trending up, is typically notchanging as rapidly as the actual value. The output of the comparisonsof coherence, amplitude or energy, or a combination thereof tothresholds including, for example, whether coherence is above aparticular minimum value or below a particular maximum value, whethercoherence values are increasing or decreasing, whether energy of thesignal is above a minimum signal or below a maximum signal, or the like,or some combination thereof, is used to generate control output signal,step 164. The control output signal may provide 2 or 3 dimensionalcontrol signals, such as, for example, a thrust, rotation, and liftsignal, multiple thrust signals, a movement vector in the X,Y,Z plane, amovement vector in the X,Y plane, any other type of movement vector, orthe like to control an object, step 165. Some exemplary operational andfunctional steps associated with developing the various control signalsof a present prototype are provided below. Throughout the process ofmethodology 150, a display screen or monitor 119 may provide variousdisplay various signals, step 166. An exemplary display output will beexplained further below in connection with FIG. 7. The display outputmay be used as visual feedback to the patient 108. The display mayinclude, for example, raw brainwave EEG signals, coherence signals,control signals, threshold displays, and the like as will be explainedin connection with FIG. 7. During the operation of methodology 150, andgenerally in relation to coherence values and threshold values, abinaural beat may be generated, step 168. The binaural beat generatednon-cognitively provides feedback tending to influence the patient'sbrain to produce desired brainwaves or brainwave patterns. A neuralnetwork (sometimes referred to conventionally as auto-logic cascade orfuzzy logic) monitors the system, for example, the coherence, amplitudeor energy, or thresholds or a combination thereof, step 170. Based onthe monitoring, it is determined whether the desired brainwave functionsare being obtained, step 172, i.e., or the desired thresholds are beingmet or the like. If it is determined that the thresholds are not beingmet or being met too readily, the threshold is adjusted based on apredetermined adjustment algorithm, step 174. For example, one possiblethreshold consideration is a determination regarding whether Alpha Thetabrainwave coherence is increasing. The threshold for this value may beadjusted depending on whether the patient is successful in causing theincrease in coherence to exceed the required threshold. If, for example,it is determined the patient cannot successfully obtain the Alpha Thetacoherence at the initial threshold, the threshold is lowered (or raised)until the patient can in fact meet the threshold.

The feedback loop described above as an auto logic cascade includescomponents of fuzzy logic that combines a simple logic feedback systemswith a linear feedback system to adjust the threshold values associatedwith the coherence and energy levels. These controls are used to adjustboth the input threshold controls and the output threshold controlassist. For example, one therapeutic goal is to increase Alpha Thetacoherence. If Alpha Theta coherence is low such that the initialthreshold is not exceeded within a predetermined timeframe or notexceeded and maintained for a predetermined timeframe, the fuzzy logicfeedback loop may in the first instance allow the threshold variationand in the second instance reduce the required coherence threshold to anachievable level. The amount the threshold is lowered may be, forexample, based on an iterative process until the threshold is achieved,for example, or on other control algorithms. Similarly, the feedbackloop may increase the threshold if the coherence value exceeds thethreshold by a certain amount, consistently for a predeterminedtimeframe, or overly rapidly, some combination thereof or the like.

Another exemplary feedback may be related to another therapeutic goal ofincreasing Alpha Theta coherence while decreasing Beta coherence(frequency separation). In the first instance, the feedback loop woulddetermine whether Alpha Theta coherence is satisfactorily exceeding itsthreshold. Next, the feedback loop would determine whether theseparation between the coherences is above a desired separation.Depending on the adjustment algorithm, the separation threshold may beincreased or decreased. Changing the threshold of frequency separationmay influence whether the initial thresholds are still beingsatisfactorily achieved.

Thus, as can be seen, the feedback loop in one exemplary embodimentreacts to a series of inputs to adjust threshold values. The first inputshould be performed satisfactorily before evaluating and adjusting thesecond input, etc. Moreover, each adjustment causes a reevaluation ofthe preceding thresholds to compensate for any influence that adjustinglater thresholds may have on previous thresholds. While identified assequential in nature, the feedback loop may operate sequentially, inparallel, or a combination. Moreover, multiple feedback loops may beestablished to control for various desired results. For example, in themethod 150 described, separate feedback loops or cascades may beestablished for each of the coherence, amplitude, etc. measurements.

One of ordinary skill in the art will recognize on reviewing the abovethat display output (step 166) and control object (step 165) providevisual feedback to the patient. For example, if increased Alpha Thetacoherence causes lift of an object, the object's movement up or downwould provide visual, cognitive feedback to the patient. Similarly,display output would function in a similar way. As will be explained inconnection with FIG. 7 below, certain features of the display output mayincrease the ability of the patient to control the real object althoughthe therapeutic effect and training of the brain is generally enhancedby control of the real object. Steps 168 to 174 generally provide anon-cognitive feedback. A binaural beat generator provides neurologicalfeedback by providing low resonance frequency sound waves thatfacilitate the brain's producing the desired patterns. The neural netadjusting the thresholds provides feedback in making the actions easieror harder to assist in training the brain.

Referring now to FIGS. 1 and 3-7, one exemplary embodiment of atherapeutic system 100 capable of performing the methodology 150described above is provided. Therapeutic system 100 will generally bedescribed in functional block diagrams and the like. One of ordinaryskill in the art on reading the disclosure will appreciate that thevarious illustrative functional blocks, modules, circuits, and algorithmsteps described in connection with the embodiments disclosed herein maybe implemented as electronic hardware, computer software, orcombinations of both. To clearly illustrate this interchangeability ofhardware and software, various illustrative components, blocks, modules,circuits, and steps have been described above generally in terms oftheir functionality. Whether such functionality is implemented ashardware or software depends upon the particular application and designconstraints imposed on the overall system. Skilled artisans mayimplement the described functionality in varying ways for eachparticular application, but such implementation decisions should not beinterpreted as causing a departure from the scope of the presentapplication.

Referring first to FIG. 3, the raw electrical signal obtained by sensors102 is converted to a format usable by computer processors or the like.To convert the raw brainwave signals sensed by sensors 102, brainwavesignals 116, 118 are transmitted to a conventional electroencephalogram(EEG) 202, or similar device. The EEG 202 amplifies the electricalsignals using amplifier 204 and digitizes the signals using an analog todigital (ADC) 206 to produce amplified, digitized brainwave signals 208,210. The EEG 202 may be separate as shown or integrated into controlprocessor 104.

The control processor 104 will now be described with reference to thefigures. Each of the parts identified in figures may be individualcomponents, integrated into a single component, or integrated into moreor fewer components. Referring first to FIG. 4, the amplified, digitizedleft brainwave signal 208 and the amplified, digitized right brainwavesignal 210 are input to a coherence module 302. Coherence module 302 ingeneral provides a coherence value between the two brainwave signals—inthis case the left hemisphere brainwave signal and the right hemispherebrainwave signal although any two brainwaves are possible. The coherencemodule 302 provides coherence for each of the various types ofbrainwaves. Thus the amplified, digitized brainwave signal 208 is inputthrough a series of band pass filters 303, 304, 305, and 306. More orfewer filters may be used as a matter of design choice depending on whatvalues are to be measured for coherence. As shown, the filters are shownin parallel to provide four distinct outputs for each amplified,digitized brainwave signal 208, 210. The band pass filters 303-306 maybe individual filters, a software module, or integrated into a singledevice, such as a microchip or the like. Filter 303 passes a firstfrequency band, filter 304 passes a second frequency band, filter 305passes a third frequency band, and filter 306 passes a fourth frequencyband. One exemplary first frequency band may be 6 to 11 Hz. The 6 to 11Hz comprises Alpha Theta brainwaves. One exemplary second frequency bandmay be 12 to 15 Hz. The 12 to 15 Hz comprises sensory motor rhythmbrainwaves, sometimes referred to as SMR or “low Beta.” One exemplarythird frequency band may be 18-28 Hz. The 18 to 28 Hz comprises Betabrainwaves. One exemplary fourth frequency band may be 37 to 49 Hz. The37 to 49 Hz comprises Gamma brainwaves. The exemplary frequency bandsshown are smaller than the traditional ranges for the respectivebrainwaves, for example, the Alpha Theta range is typically consideredto extend from about 4 Hz to about 12 Hz. The reduced frequency range ofeach band, however, allows a frequency separation between the bands. Thefirst exemplary frequency band, may be, for example, 4 to 12 Hz to coverthe entire traditional range of the Alpha Theta brainwaves, but perhapswould need to be reduced to provide a clear separation between thefrequency bands. While the frequency ranges and number of comparisons isa matter of specific design choice and functionality desired, thetechnology of the present application will generally be explained withreference to the four frequency ranges provided above recognizing thatmore, less, and different frequency ranges are possible over thespectrum of brainwave frequencies. The chosen frequency ranges is inpart based on current understanding of the brain and its functionindicating that in general wellness is increased with increasedcoherence among Alpha Theta brainwaves and decreased coherence amongBeta brainwaves.

Once the frequency signals are separated into one left amplified,digitized first frequency range signal and one right amplified,digitized first frequency range signal, the one left amplified,digitized first frequency range signal and the one right amplified,digitized first frequency signal are input into a first frequency rangecoherence generator 308. In this case, the first frequency range is theAlpha Theta range. Thus, the first coherence generator 308 is generatingan Alpha Theta coherence signal 310. Conventionally, coherence isdetermined by comparing the values of cross-power spectrum from one waveform to another wave form in the frequency spectrum. Thus, the firstcoherence generator 308 may include a Fourier transform generator 312,and such a transformation may be completed using, for example, aquadrature filter as is generally known in the art. As this example hasbeen arranged with four frequency ranges, the coherence module 302includes four coherence generators 308 (as explained above), 314, 316,and 318, which could be separate hardware, software modules, orintegrated into single parts. Coherence generators 314, 316, and 318 maysimilarly include a Fourier transform generator 312 although other typesof coherence may be measured instead of power of the waveform. Coherencegenerator 314, in this example, provides a SMR coherence signal 320.Coherence generator 316, in this example, provides a Beta coherencesignal 322. Coherence generator 318, in this example, provides a Gammacoherence signal 324. The designations Alpha Theta coherence, SMRcoherence, Beta coherence, and Gamma coherence used herein are forsimplicity of reference. The particular frequency ranges selected inthis example should not be considered limiting. By way of reference,coherence generally can be considered a value between 0% coherence (ordiscoherence or out of synch or morphologically dissimilar) to 100%coherence, which indicates identical wave forms.

Amplified, digitized brainwaves 208, 210 also may be combined by anamplitude generator 326 to form a total brainwave amplitude signal 328.While shown separate from coherence module 302, amplitude generator 326may be integrated with the coherence module 302 as it also uses theamplified, digitized brainwaves directly from EEG 202. The amplified,digitized brainwaves 208 and 210 may be displayed on a display 330, suchas a conventional monitor, LCD screen, flat screen, or the like.Displaying the amplified, digitized brainwaves 208 and 210 may indicatewhether, for example, an electrode is properly oriented, whether thecables or transmitters are properly connected, or the like.

The coherence signals, Alpha Theta coherence signal 310, SMR coherencesignal 320, Beta coherence signal 322, and Gamma coherence signal 324are transmitted to a threshold module 332 portion of control processor104. Again, threshold module 332 may be integrated into a single controlprocessor 104 or a separate, networked control processor 104. In thisexemplary embodiment, threshold module 332 determines whether particularcoherences are, for example, above a certain minimum threshold, below acertain maximum threshold, or between a certain minimum and maximum, andwhether they are increasing, are decreasing, or some combinationthereof, such as, for example, above a certain minimum threshold andincreasing. Generally, threshold module 332 comprises a series ofcomparators to provide a modified output data stream based on thecomparisons made. In one exemplary embodiment, threshold module 332comprises at least one comparator for each coherence determination.Thus, threshold module 332 may include comparators 334, 336, 338, and340. In this exemplary embodiment, comparator 334 makes a firstthreshold determination based on comparing a present Alpha Thetacoherence to the previous Alpha Theta coherence. If the comparisonindicates coherence is increasing, the comparator outputs an IncreasingAlpha Coherence signal 342. If coherence is constant (unlikely) ordecreasing, the comparator does not output the Increasing AlphaCoherence signal 342. In this exemplary embodiment, comparator 336 maydetermine decreasing Beta coherence to output a Decreasing BetaCoherence signal 344. Comparator 338 may determine a SMR Coherence tooutput an Increasing SMR Coherence signal 346. Finally, comparator 340may determine a Gamma coherence to output an Increasing Gamma Coherencesignal 348.

In one working embodiment of the technology of the present application,a useful threshold calculation includes band ratio determinations. Theratio is calculated by a ratio generator 350 in threshold module 332that generates at least one first ratio signal 352. First signal 352 maybe a ratio caparison of one frequency range to another, for example, theamplitude of the Alpha frequency range divided by the amplitude of theTheta frequency range. Other ratios, more, and different ratios arepossible. In this example, the amplitude signals to generate the ratiomay be received from amplitude generator 326. The first signal 352 maybe used, for example, as a control input, sometimes referred to as anauto threshold. When the first signal 352 exceeds a predefinedthreshold, a positive or achieved state is output by threshold module332. The positive or achieve state allows the feedback mechanism, i.e.,the auto logic cascade identified above to monitor and adjust thresholdvalues based on predefined satisfactory response criteria. Conversely,when the negative, null, or not achieved state is output by thresholdmodule, the thresholds associated with threshold module 332 are locked.Ratio generator 350 also may output an indication regarding whether theratio between the ranges is increasing, decreasing, or remaining steady.

As one of ordinary skill in the art would now recognize on reading thedisclosure herein, coherence between separate brainwaves changes rapidlyand moment to moment. By inputting the coherence signal to compare thesignal to a threshold, the rapidly changing brainwave coherence is lessproblematic for a control signal as the coherence typically tends toincrease or decrease at a less rapid rate. Additionally, the thresholddetermination may be based on a hysteresis, such that, for example, oncethe threshold is achieved at a first predetermined level, it will not beturned off (or unachieved) until a second predetermined level isreached. Furthermore, the actual threshold values themselves becomeuseful output since they take advantage of the hysteresis inherent inthe neural net aspect of this design. Moreover, as can be appreciated byone of ordinary skill in the art on reading the disclosure, increasingAlpha Theta coherence while decreasing Beta coherence is generallyconsidered a good therapeutic result. Thus, the above thresholds arelargely based on therapeutic considerations and not necessarily on, forexample, game simulations, prosthesis control, or the like. In otherwords, more, less, different, other, or a combination of these and otherthreshold determinations are possible depending on the desired output ofthe system. Other threshold values may include, for example, an AlphaTheta phase synchronization relating to the phase difference between thefirst and second brainwave signals being measured. Still other thresholdvalues may include, for example, total or individual wave amplitudethreshold values. Yet other thresholds may compare relative coherence ordiscoherence between ranges, such as a ratio between Alpha Thetacoherence as compared to Beta coherence. These examples of variousthresholds are simply that, and should be considered exemplary andnon-limiting examples.

The outputs of threshold module 332 are provided to a perceptual controltheory (PCT) processor 360. PCT processor 360 combines the inputs fromthreshold module 332, coherence module 302, amplitude generator 326,other, less, more, or different inputs as desired into a PCT controloutput stream 362 usable to a portion of the display, which will beexplained further below with regard to FIG. 7. PCT control output stream362 is generally understood in the art and will not be further explainedexcept as necessary to explain the present invention. As shown in thefigure for simplicity, PCT processor 360 is shown as receiving inputfrom threshold module 332 only.

PCT processor 360 can generate a control signal using a number ofdifferent calculations relating to the thresholds and coherence valuesmeasures as described above. The inputs are processed by the PCTprocessor 360 to output a video signal as described in conjunction withFIG. 7 below. The video signal is designed to influence brain functionto produce the desired brainwave activity. Some exemplary calculationsinclude, equation 1:

(Increasing Alpha Theta Coherence signal 342−Decreasing Beta Coherencesignal 344)divided by(Increasing Alpha Theta Coherence Signal342+Decreasing Beta Coherence Signal 344)

This is the equation for Frequency Separation and is the input tothreshold 3 (noted below). This is one of the values inserted into thePCT data stream. This equation is not necessarily in and of itself a PCTcalculation.

Equation 1 returns a value between −1 and 1. The closer the value to 1indicates the greater relative difference between Alpha Theta Coherenceand Beta Coherence. Increasing Alpha Theta coherence while decreasingBeta Coherence is considered to have a positive therapeutic effect.

The result of equation 1 also is used to generate a Frequency Separationsignal 356 by a frequency separation generator 354 in threshold module332. In particular frequency separation generator 354 produces thefrequency separation signal 356 if the result of equation 1 showsincreasing dominance of Alpha Theta Coherence over Beta Coherence.

Absolute value of(Increasing Alpha Theta Coherence signal 342−DecreasingBeta Coherence signal 344)  Equation 2

Equation 2 returns an absolute value of the difference between AlphaTheta Coherence and Beta Coherence.

(Increasing Alpha Theta Coherence signal 342−Decreasing Beta Coherencesignal 344)*100  Equation 3

(Increasing Alpha Theta Coherence signal 342)*100  Equation 4

(Decreasing Beta Coherence signal 344)*100  Equation 5

The PCT processor 360 further defines a continuous PCT control outputstream 362 by selecting the minimum value of 300 or the result of (notethe absolute values of each additive is taken in the equation 6 below):

((Increasing Alpha Theta Coherence ratio signal 342+Decreasing BetaCoherence ratio signal 344+frequency separation ratio signal 356+firstratio signal(Alpha/Theta ratio)352+Increasing SMR Coherence signal348)*29

The PCT processor 360 may be increased (make control more difficult) ordecreased (make control easier) by adjusting the multiplier “29” up ordown. Generally the multiplier may be adjusted between about 25 to 33.The control output signal 118 sends a positive value, in the aboveexemplary embodiment, when the value of equation 6 is greater than 160and a negative value or “0” when the value of equation 6 is less than160. Again, the difficulty of control may be modified up or down byadjusting the value up or down from 160. Generally, value 160 may beadjusted between about 145 (easy) to about 175 (hard) to adjust control.

The PCT control output stream 362 provides visual feedback relating to avirtual object as will be explained below. The virtual object providesvisual feedback to patient 108.

Another form of feedback provided is a binaural beat, which is anon-cognitive feedback. Referring back to FIG. 1, one or more speakersor tone generators 120 are provided about user 108. FIG. 5 shows abinaural beat generator 400 in more detail. Binaural beat generator 400includes a plurality of tone generators 402, 404, 406, and 408. Thepaired tone generators are used to provide interference of a particularbinaural beat. In this exemplary embodiment, tone generator 402 providesa 60 Hz signal to, for example, the right ear of user 108 and tonegenerator 404 provides a 68 Hz signal to, for example, the left ear. Theinterference of the tone generators 402 and 404 provides a binaural beatfrequency of 8 Hz. Tone generator 406 provides a 104.1 Hz signal to, forexample, the left ear. Tone generator 408 provides, a 149.1 Hz signalto, for example, the right ear. The interference of the tone generators406 and 408 provide a binaural beat frequency of 45 Hz. Other binauralbeats may be used as desired. The binaural beats produced by binauralbeat generator provide an induced resonance frequency causingnon-cognitive feedback to the user via the ears, body and head and tendto assist in the formation of the desired brainwave coherence. In thisexample, tone generators 402, 404, 406, and 408 are activated by a firstfeedback signal 410 generated by PCT processor 360 indicating thatequation 3 is approaching 1. In general, the tones produced by thebinaural beat generator 400 may include more or less tones, butproducing three tones has been satisfactory for a prototype. Thefrequencies of the three tones include 6 Hz (Theta), 8 Hz (Low Alpha) &45 Hz (High Gamma). Each tends to assist improving coherence at or nearits own frequency. The 6 & 8 Hz tones are generated when FrequencySeparation is decreasing (below threshold). The 45 Hz tone is generatedwhen Frequency Separation is above threshold. Binaural beat generator400 further includes an audio player 412 providing a 6 Hz signal. Thesignal using a transfer function to move the sound to provide theillusion of a moving sound source.

Referring now to FIG. 6 a control module 500 is provided. Control module500 may be integrated into control processor 104 or a separate unit as amatter of design choice. Control module 500 receives a series of inputsfrom threshold module 332 and uses those inputs to generate the controloutput signal 118. Control module 500 is separated into three componentsto provided three dimensional control signals, which in this exemplaryembodiment include a vertical control module 500A, a rotational controlmodule 500B, and a thrust control module 500C. Other control modules toprovide three dimensional control signals are possible.

Control module 500 includes vertical control module 500A. Verticalcontrol module 500A includes an averaging component 502. Averagingcomponent 502 receives input indicative of the actual value of thethreshold as determined by the neural net over a long and short window,i.e., dynamic averaging. A long window of about 120 seconds and a shortwindow of about 5 seconds provide satisfactory results. Although theabove identified windows have proven satisfactory with a prototype, thewindow was empirically determined to provide the smoothest possibleresponse once again riding the hysteresis latency identified above. Theprecedent for this type of calculation goes back to the early days ofbiofeedback when temperature training was accomplished by feeding backonly the rate of change information while ignoring absolute values. Inthis case increasing the degree of improvement provides increasedvertical lift. Referring back to FIG. 4, threshold module 332 includes,for example, a comparator 334 that outputs a positive response when itis determined that the Alpha Theta coherence is increasing, i.e., theIncreasing Alpha Coherence threshold signal 342. In this exemplaryembodiment, control module 500 uses averaging component 502 to averageIncreasing Alpha Coherence threshold signal 342 over a long window,Decreasing Beta Coherence threshold signal 344 over a long window,Increasing Alpha Coherence threshold signal 342 over a short window, andDecreasing Beta Coherence threshold signal 344 over a short window.

The desired dynamic averaging of threshold signals 504 generated byaveraging component 502 are provided to ratio component 506. Ratiocomponent 506 creates a dynamic ratio of ratios by dividing the ratio ofthe short window moving average by the long window moving average andoutputs a first control signal 118 ₁ based on the ratios. In thisexemplary embodiment, the first control signal is used as a verticalcontrol signal, i.e., lift. The first control signal 118 ₁ provides acontrol value to both a virtual object and a real object in this case toprovide lift indication. Anecdotal evidence suggests lift may be theportion of the system most responsive to training. Averaging component502 and ratio component 506 may be referred to as first or verticalcontrol module 500A.

Control module 500 comprises a second or, in this example, rotationalcontrol module 500B. Rotational control module 500B receives first ratiosignal 352 from threshold module 332 and uses a first comparator 508 todetermine whether the first ratio signal 352 satisfies a firstpredetermined threshold, either above a minimum, below a maximum, or acombination thereof. In one exemplary embodiment, comparator 508 outputsa high signal on successfully meeting a high threshold value and outputsa low signal on successfully meeting a low threshold value. To maintainseparation between vertical and rotational controls, vertical control isbased primarily on coherence without amplitude considerations androtational control is determined on the basis of calculating theamplitude or energy ratios. In the exemplary prototype, Alpha amplitudedominance will produce a clockwise spin control 118 c and Thetaamplitude dominance will produce a counter-clockwise spin control 118cc. Relative Alpha/Theta balance (not necessarily parity) will stabilizespin. Spin may function by determining the “SPIN” or “NO SPIN” statebased on 1) the Alpha/Theta amplitude ratio, 2) the altitude, and 3) ifthe round is in the first 30-seconds. Otherwise no rotational controlsignal is output.

Control module 500 includes or comprises a thrust control module 500C.Thrust control in this exemplary embodiment uses one or more comparators512 to determine whether all the generated coherence signals are above(below) certain minimum threshold values. For example, comparator(s) 512may determine whether the following signals meet a predetermined minimumcoherence level:

Increasing Alpha Theta Coherence signal 342.

Increasing SMR Coherence signal 346.

Increasing Gamma Coherence signal 348.

Decreasing Beta Coherence signal 344.

Based on the combined determination that coherence is above a minimumvalue and other signals determined from the actual threshold values andcombined in a logical statement, thrust control module 500C outputs athrust control signal 118 _(T). For example, if the combined coherenceis above a minimum value and it is determined the rotation of the deviceis zero (i.e., signals 118 _(c) and 118 _(cc) are below values to causerotational movement), thrust control signal 118 _(T) may be positive toprovide forward motion of control object 106.

Regarding the controls, the logic cascade described above also providesan output assist/control feature. In particular, the feedback loop mayassist or control the ability of the control object, whether virtual orreal, to move. For example, the system may first confirm the verticalcontrol module 500A has provided a particular amount of lift as definedby the control signal 118 ₁, which amount may vary between 0% lift to100% lift. Once a predetermined amount of lift is determined orprovided, rotation control module is enabled and allowed to provideeither clockwise rotation or counter clockwise rotation. If the rotationcontrol is enabled and providing a rotational control signal, i.e., thecontrol object is rotating clockwise or counter clockwise, then thrustcontrol module 500C is disabled. If rotational control module 500B isenabled, but the rotational signal is below a predetermined amount ofrotation, which may be set as low as zero rotation or as high as fullrotation, the thrust control module 500C is enabled to provide thrust tocontrol object 118 to cause movement through a 3 dimensional space(whether real or virtual).

Referring now to FIG. 7, an exemplary display 700 associated with thetherapeutic system 100 is provided. Display 700 is visually shown ondisplays 119 (FIG. 1, or a display 330 FIG. 4). Display 700 includes anumber of displays that assist patient 108 and/or a therapist tofacilitate use of therapeutic system 100. For example, display 700 mayinclude a vertical lift display 702 shown as a simple bar graph and analtitude simulator 704. Altitude simulator 704 may display a videorepresentation 706 of the control object 106. Generally, the liftdisplay 702 and altitude simulator 704 may display similar informationin different formats relating to the vertical lift of control object106. In some cases, the video display may assist a patient 108 maintainflight status when they have difficulty controlling the real object.While two formats are shown, other similar displays could be provided.Display 700 also may provide an indicator 708 of the number of timespatient 108 has used the therapeutic system 100, which could be thenumber of times in a single session or the number of times over multiplesessions, as well as a timer display 710, which displays for example thetime of a current flight use, the total time of a current session, or atotal time across multiple sessions. Display 700 also includes acalibration display 712. The Calibration display measures time duringthe first 30 seconds of each round during which time all controls areoff-line while the computer 1) re-establishes a baseline and 2)initializes one of the drive chips on the UFO receiver. Display 700further includes a video display 714 of all control object movementcontrol output signals 118 include a display associated with lift 714L,rotation 714R, and thrust 714T, which may correspond to an animation 716and graph 718. Animation 716 would animate the control signals 714L,714R, and 714T onto an animation of control object 106. Similarly, graph718 would show separate traces 718L, 718R, and 718T over time of thecontrol output signals 118. Display 720 is a bar graph associated withforward speed of control object 106. Display 722, 724, 726, 728, and 730display numerical values associated with each of thrust, power, lift,PCT value and threshold difference. The Threshold Difference Displayshows the mathematical difference between the threshold values underconsideration. Display 700 also includes a frequency separation videodisplay 732.

PCT video display 734 provides the PCT control output stream 362 togenerate indicator 736. PCT control output stream 362 places indicator736 at a location on display 734 such that patient 108 should attempt tolocate indicator 736 using eye contact. By concentrating on movingindicator 736 to the center, for example, of display 734, the patient'sbrainwaves are influenced in the proper direction.

Display 700 also may include displays related to the various thresholdand calculations such as display 738 (thrust control), display 740(rotation), display 742 (Increase Alpha Theta Coherence), display 744(Decreases Beta Coherence), display 746 (gate status), display 748(frequency separation), and display 750 (Increase Gamma Coherence).

One working embodiment of the technology of the present application isprovided. This summary is broken down into modules, similar to theabove, including modules 1-10. Modules 1, 2 and 3 may be referred to asstage one of the overall technology described above and includeconverting the raw EEG signal into meaningful data streams. Modules 4,5, 6, 7, 8, and 9, may be referred to as stage two of the overalltechnology described above and include encoding the data streams intodiscreet data usable as biofeedback. Module 10 may be referred to asstage three of the overall technology described above and includesdynamic interaction using the adaptive and self-adjusting calculationsas described. Finally, a stage four of the overall technology, notrepresented by a module, may be described as the output to control aremote device. Moreover, the various algorithms may be considered as thecoherence values established in Module One are passed to thresholdobjects that calculate: 1) pass/fail states modified by input from theneural net, 2) threshold to signal ratios. The neural net described inModule Ten is an adaptive logic cascade that links all the thresholdobjects together into a dynamic decision making network.

The software algorithms are composed of the following elements:

-   -   1. Four coherence bandwidths    -   2. Coherence frequency separation/dispersion calculation    -   3. Neural Net linking auto threshold switches into a        rational/adaptive decision tree    -   4. Novel calculation (neurogistic euphonics) of binaural beats        frequencies to produce IRF (Induced Resonant Frequencies)    -   5. Use of IRF as subliminal feedback loop including tactile        feedback    -   6. PCT (Perceptual Control Theory) data streaming    -   7. Two-branch alternating feedback decision tree    -   8. Output (Piloting) algorithms (cerebotic response):        -   a. RATIO OUTPUT (Vertical): Regression of regressions            analysis of Alpha-theta/Beta neural net output        -   b. AMPLITUDE BALANCE OUTPUT (Rotational): Simple Alpha/Theta            amplitude ratio        -   c. DIRECT PCT OUTPUT (Forward thrust): Total of Neural Net            control outputs

Module I

Inputs & Coherence Calculations

Module One receives the two external EEG inputs, filters the signal fornoise and performs all initial mathematical transforms. This module iscomprised of a total of 13 objects: 2 EEG input devices, 4 filters, 2spectrum analyzer display objects, 4 coherence objects, and an equationcalculation object.

The initial signal processing is the active calculation from the two EEGinputs of coherence for four separate bandwidths: AlphaTheta 6-11 Hz;SMR 12-15 Hz; Beta 18-28 Hz; Gamma 37-49 Hz.

Coherence is dynamically calculated within the BioExplorer softwarepresumably using the outputs of a quadrature filter (90° phase shiftconverting sine into cosine) inserted into the basic coherence equationidentified as the cross-power spectrum divided by the sum of theautospectra.

One definition of the mathematics for coherence was provided by RobertW. Thatcher, Ph.D., et al. in “An EEG Severity Index of Traumatic BrainInjury”, J. Neuropsychiatry Clin. Neurosci. 13:77-87, February 2001,“Coherence is defined as

${\Gamma_{xy}^{2}(f)} = \frac{\lbrack {G_{xy}(f)} \rbrack^{2}}{\lbrack {{G_{xx}(f)}{G_{yy}(f)}} \rbrack}$

where G_(xy)(f) is the cross-power spectral density and G_(xx)(f) andG_(yy)(f) are the respective autopower spectral densities.” “Thecomputational procedure to obtain coherence involved first computing thepower spectra for x and y and then computing the normalizedcross-spectra. Because complex analyses are involved, this produced thecospectrum (r for real) and quadspectrum (q for imaginary). Thencoherence was computed as

$\Gamma_{xy}^{2} = {\frac{r_{xy}^{2} + q_{xy}^{2}}{G_{xx}G_{yy}}.}$

Further mathematical details of the analyses are provided elsewhere.”

The Design Objects of this module are as follows. Input includes Source1 & Source 2 that receive the raw EEG data via serial, parallel or USBinterface through COM1 or data port. Filters provided as Filter 1 Filter3 are highpass 6th order Butterworth filters and permit frequenciesabove 1.50 Hz and Filter 2 & Filter 4 are lowpass FIR filters of length80 and permit frequencies below 56 Hz.

Coherence Calculators are provided. The coherence calculators provide:

Coherence 1 (6-11 Hz, AlphaTheta Up) uses a Chebyshev IIR 3^(rd) orderfilter with a 100.0 m ripple.

Coherence 2 (18-28 Hz, Beta Down) uses a Butterworth IIR 6^(th) orderfilter.

Coherence 3 (12-15 Hz, SMR Up) uses a Butterworth IIR 6^(th) orderfilter.

Coherence 4 (37-49 Hz, Gamma Up) uses a Butterworth IIR 6^(th) orderfilter.

The Calculation Object of Module One is

Expression 7 (Band Ratio Calc) which combines the two raw EEG signals bysimple addition for later analysis of EEG amplitude ratios.

Visual Display Objects include a Spectrum Analyzer 1 (RHemisphere) &Spectrum Analyzer 2 (LHemisphere) which use Hann filtering with 128 Binsand a refresh rate of 100 ms. The display range is set to recordamplitude activity from 2 to 50 Hz with a sensitivity of 20.0microvolts. These displays provide information used to determine signalstrength, electrode status and artifact status in real time.

Module II

Module II includes BASIC THRESHOLD CALCULATIONS. This can be consideredone of the primary processors for the incoming coherence data fromModule I and establishes the essential calculations on which the rest ofthe design depends. Module Two is adaptive and is both part of and underthe control of the primary neural net configuration (Module Ten).

This module II is comprised of a total of 11 objects: 8 calculationobjects and 3 display objects. The calculation objects include 7threshold calculation devices and one band ratio calculation device. Thedisplay objects include 2 bar graph displays and one trend graphicdisplay. The bar graphs are shown in FIG. 7.

Calculation Objects

Threshold 1 (Incr Alpha Coh) receives coherence input from Coherence 1(AlphaTheta Up) and calculates success based on increase only at a basetolerance of 40%. The auto-threshold function receives controlactivation from NOT 1 (NOT Sep) described in Module 3.

Threshold 2 (Decr Beta Coh) receives coherence input from Coherence 2(Beta Down) and calculates success based on decrease only at a basetolerance of 55%. The auto-threshold function receives controlactivation from the Pass/Fail output of Threshold 4 (Alpha Sync).

Threshold 3 (Freq Sep) receives input from an Expression device (FreqSep Calc) described in Module 3 and calculates success based on increaseonly at a base tolerance of 75% with 200 ms averaging. Theauto-threshold function receives control activation from the Pass/Failoutput of Threshold 1 (Incr Alpha Coh).

Threshold 4 (Alpha Sync) receives input from the Phase Difference outputof Coherence 1 and calculates success based on double thresholds at abase tolerance of 63% for both upper and lower limits. Theauto-threshold function receives control activation from the Pass/Failoutput of Threshold 3 (Freq Sep).

Threshold 5 (A-T Ratio) receives input from the output of BandRatio 1and calculates success based on double thresholds at a base tolerance of75% for both upper and lower limits with 500 ms averaging. Theauto-threshold function receives control activation from the Pass/Failoutput of Threshold 3 (Freq Sep).

Threshold 6 (SMR COH) receives input from Coherence 3 (SMR Up) andcalculates success based on increase only at a base tolerance of 60%with no averaging. The auto-threshold function receives controlactivation from the Pass/Fail output of Threshold 5 (A-T Ratio).

Threshold 7 (Gamma COH) receives input from Coherence 4 (Gamma Up) andcalculates success based on increase only at a base tolerance of 75%with no averaging. The auto-threshold function receives controlactivation from the output of an Expression device described in Module 3that calculates a fixed threshold of PCT data.

Band Ratio 1 receives input from the Expression device Band Ratio Calcin Module 1 and calculates the power ratio as:

[Amplitude(8.0-10.0 Hz)]divided by[Amplitude(6.0-8.0 Hz)]

with a resolution of 1 Hz. Output from this device goes to bothThreshold 5 (A-T Ratio) and Threshold 9 (Rotation Index: A/T Ratio).

The display objects from the above include a bar graph of (Incr AlphaCoh) displays the signal output from Threshold 1 (Incr Alpha Coh) withno averaging and a 35 ms refresh rate in a range of 0-1 and a bar graphof (Decr Beta Coh) displays the signal output from Threshold 2 (DecrBeta Coh) with no averaging and a 35 ms refresh rate in a range of 0-1.

A trend is provided as (Alpha Coh Dom) combines the coherence outputs ofCoherence 1 and Coherence 2. Coherence 1 appears in red as a solid area,and Coherence 2 appears as the background color. In this manner the onlydata represented will be when Coherence 1 exceeds the value of Coherence2. The coherence output of Threshold 6 is also represented as a simpleline graph. Averaging is 10 secs with a display range of 30 minutes and300 second delineations to identify each 5-minute period.

Module III

Module III provides calculations for band ratio, frequency separation,PCT, and an AND gate. Module Three creates the three data streams onwhich the neurofeedback and the pilot controls are based. This modulecompletes Stage One.

This module III is comprised of a total of 16 objects: 9 calculationobjects, a Boolean NOT gate, a Boolean OR gate and 5 display objects.The calculation objects include 8 equation devices and one thresholddevice. The display objects include 2 trend graphic displays and 3numeric readouts.

Calculation Objects include:

Threshold 8 (GATE STATUS) receives input from Expression 14 (GATE) andcalculates success based on increase only above a threshold with a fixedvalue of 7. The pass/fail output is used as a limiting factor inExpression 43 (Low Power Assist), controls the auto-threshold functionof Threshold 9 and triggers Continuous MIDI 1 (Event Bell). Theauto-threshold input function is not used.

Expression 1 (FreqSepCalc) receives inputs from the coherence outputs ofCoherence 1 (In1) and Coherence 2 (In2) and processes these data asfollows:

(Alphatheta Coherence−Beta Coherence)divided by(AlphathetaCoherence+Beta Coherence)

A value in the range of −1 to +1 is returned with the higher valueindicating a greater relative difference between AlphaTheta coherenceand Beta Coherence.

Expression 3 receives threshold data from Threshold 4 (AlphaSync) withthe low threshold values going to In2 and the high threshold valuesgoing to In1. The equation,

ABS(In1−In2)

returns the absolute value of the difference between the two thresholdsand sends this information to numeric readout Meter 6 (AlphaSyncDiff).

Expression 4 receives data from the low threshold outputs of Threshold 1(In1) and Threshold 2 (In2) and processes the data through the equation:

(In1−In2)*100 (NOTE: *=multiply)

which is then sent to both numeric (Meter 7, Sep Value) and graphicreadouts (Trend 1, DEG of SEP).

Expression 5 receives data from the low threshold outputs of Threshold 1(In1), multiplies it by 100 and sends these data to Trend 1 (DEG ofSEP).

Expression 6 receives data from the low threshold outputs of Threshold 2(In1), and multiplies it by 100 and sends these data to Trend 1 (DEG ofSEP).

Expression 8 (PCT Calculation) receives input from the ratio output of 5threshold objects to create a continuous data stream. The ratio outputis calculated as

-   -   “the signal divided by the threshold value.”

In1 is from Threshold 1.

In2 is from Threshold 2.

In3 is from Threshold 3.

In4 is from Threshold 5.

In5 is from Threshold 6.

Because Threshold 5 uses double thresholds as limits, the ratio outputfrom Threshold 5 is calculated as:

“(the signal minus the low threshold value)divided by(the high thresholdvalue minus the low threshold value).”

The PCT equation is embedded in a logical statement:

MIN(ABS(In1+In2+In3+In4+In5)*29),300)

and returns the lower value of EITHER the absolute value of the sum ofall 5 ratio inputs multiplied by a correction constant with a value of“29” OR “300”.

NOTE: The perceived difficulty for the control of the PCT data streamcan be adjusted by increasing (easier) or decreasing (harder) the valueof the constant through a range of 25 through 33. The effect of thischange on the overall design will be slight. These PCT adjustments inExpressions 8, 9 & 29 are the only independent adjustments that can bemade in the design. Attempting to change other variables will imbalancethe outputs.

Expression 9 receives the PCT data stream from Expression 8 (PCTCalculation). It sends a positive value (“1”) when the PCT data streamexceeds a value of 160 and a negative value (“0”) when the PCT datastream is 160 or less. The perceived difficulty for the control of thePCT data stream can be adjusted by increasing (harder) or decreasing(easier) the constant value through a range of 145 through 175. Theeffect of this change on the overall design will be slight. These PCTadjustments in Expressions 8, 9 & 29 are the only independentadjustments that can be made in the design. Attempting to change othervariables will imbalance the outputs.

Expression 14 (GATE) receives pass/fail binary data from 8 sources asfollows:

-   -   In1=Threshold 1    -   In2=Threshold 2    -   In3=Threshold 3    -   In4=Threshold 4    -   In5=Threshold 5    -   In6=Threshold 6    -   In7=Expression 9    -   In8=Threshold 7        It processes these data by simple addition and sends out a value        with a range of 0 to 8.

Also provided in this module three are Boolean operators as follows:

NOT 1 (NOT Sep) receives the pass/fail output from Threshold 3(FreqSep), reverses it and sends it to OR 2 (NN ON) as well as ToneGenerator 1, Tone Generator 2 and Audio Player 1 which are described inModule 4.

OR 2 (NN ON) receives the outputs from NOT 1 (NOT Sep) and Expression 43(Low Power Assist) and, if either input is not “0”, sends an activationsignal to the auto-threshold input of Threshold 1 which initiates theneural net cascade.

The display objects include meter 5 (SEP %) displays the output fromExpression 1 (FreqSepCalc) in integer only percentage format with a300.0 ms refresh rate and 2 sec averaging, meter 6 (AlphaSyncDiff)displays the output from Expression 3 as whole integers representing thedifference between the high and low threshold values of Threshold 4.There is a 35.0 ms refresh rate with no averaging, and meter 7 (SepValue) displays the output from Expression 5 as whole integersrepresenting the difference between the low threshold values ofThreshold 1 minus Threshold 2. There is a 35.0 ms refresh rate with noaveraging, which are all represented in FIG. 7. Also provided is a trend(DEG of SEP) that combines the outputs of Expression 5 and Expression 6.Expression 4 appears in green as a solid area, and Expression 5 appearsas the background color (black). In this manner the only datarepresented will be when Expression 4 exceeds the value of Expression 5.The output of Expression 4 is also represented as a simple line graph(red). Averaging is 10 secs with a display range of 30 minutes and300-second delineations to identify each 5-minute period. Trend 3 (PCT)displays the output of Expression 8 (PCT Calculation) in graphic formatas a line graph. Averaging is 1 sec with a display range of 30 minutesand 300-second delineations to identify each 5-minute period.

Module IV

Module Four is the beginning of Stage Two. It is comprised of 5 objectsand is responsible for the neurogistic euphonics which is thenon-cognitive portion of the cerebotic link and is responsive to theoutput of Threshold 3 (Freq Sep). As dispersion of the coherence spectraimproves above the dynamic threshold, the 45-Hz binaural beat generator(Tone Generator 3 & Tone Generator 4) is activated providing supportwith a gamma frequency assist. As dispersion of the coherence spectradrops below the dynamic threshold, the 8-Hz binaural beat generator(Tone Generator 1 & Tone Generator 2) and the 6 Hz HRTF recoding (AudioPlayer 1) are activated providing assistance for low alpha-thetacoherence values.

The frequency of the compatible carrier wave pairs (W_(F)) for beatfrequencies (F₁, F₂) is calculated as follows:

W _(F1)=(F)² /{Int[1+(F/10)]}+(F/2)

W _(F2)=(F)² /{Int[1+(F/10)]}−(F/2)

Tone Generator 1 (Right Ear 60) emits a sine wave at 60 Hz at 70% volumeinto the right channel only. This tone is enabled by a pass signal fromNOT 1 (NOT Sep). When combined with the output of Tone Generator 2 (Left68) a binaural beat frequency of 8 Hz is formed midway between theoutput locations which acts as an IRF in the neurofeedback design.

Tone Generator 2 (Left 68) emits a sine wave at 68 Hz at 70% volume intothe left channel only. This tone is enabled by a pass signal from NOT 1(NOT Sep). When combined with the output of Tone Generator 1 (Right Ear60) a binaural beat frequency of 8 Hz is formed midway between theoutput locations which acts as an IRF in the neurofeedback design.

Tone Generator 3 (Left 104.1) emits a sine wave at 104.1 Hz at 95%volume into the left channel only. This tone is enabled by a pass signalfrom Threshold 3 (Freq Sep). When combined with the output of ToneGenerator 4 (Right 149.1) a binaural beat frequency of 45 Hz is formedmidway between the output locations which acts as an IRF in theneurofeedback design.

Tone Generator 4 (Right 149.1) emits a sine wave at 149.1 Hz at 95%volume into the left channel only. This tone is enabled by a pass signalfrom Threshold 3 (Freq Sep). When combined with the output of ToneGenerator 4 (Left 104.1) a binaural beat frequency of 45 Hz is formedmidway between the output locations which acts as an IRF in theneurofeedback design.

Audio Player 1 (6 Hz HRTF) plays a digitally recorded binaural beatpattern that outputs a 6-Hz pulse with a head related transfer function(HRTF) creating the illusion of a moving sound source. This recording isenabled by a pass signal from NOT 1 (NOT Sep). Volume is set to 100%.The file is “6 Hz hrtf++.wav.”

Module V

Module Five manages all temporal aspects of the design: timing andidentifying each five-minute round, the 30-second control delay at thestart of each round, and maintaining the high count of successfulevents. This module is comprised of a total of 25 objects: 4 equationcalculation objects, 6 counter objects, 2 sample & hold objects, 1 NOTgate, 1 OR gate and 10 display objects. The 10 display objects include 8numeric readouts, one bar graph and one continuous MIDI generator. Thismodule is controlled by the output of the OR 1 gate.

Calculation Objects include:

Expression 2 (In1+1) receives the count output of Counter 3 (PeriodCounter). By adding “1” the final output value starts at “0+1=1”.

Expression 22 (Start Delay) receives the outputs from Counter 2 (PeriodTimer) (In1) and Average 5 (In2).

If((In1<30),18000,In2)

IF In1 is less than 30 secs THEN send out the value of 18000 (PPM valueto arrest vertical lift capability) ELSE send out the value of In2 tocontrol vertical lift (Module 7).

Expression 36 (VerticalDisplayControl) receives the output from Counter2 (Period Timer) and Expression 29 (High Value Compressor), sends astart signal to the Time High element of Counter 8 (LOFT) and signalsinitiation of Video Player 4 (Vertical Prop) and MIDI 1 (Lift Tone).

Expression 37 (Reverse Timer) receives the count output from Counter 2(Period Timer), subtracts this value from 301 and sends the output toMeter 12 (Flight Time).

Boolean Operators include:

OR 1 (Timer Control) receives the output of the 2 raw EEG signals toactivate a timing routine.

NOT 4 receives the delay control output from Expression 35(FlightStartRecord), reverses it, and sends the output to the resetelement of Score 1 (F CTRL/FLT) (Module 9).

Counter Objects include:

Counter 1 (HIGH) receives the output from Threshold 8 (GATE STATUS) andmaintains a cumulative count of all events (Rising Edge) each time apositive (“1”) signal is received. These events are defined by Threshold8 as

-   -   “7 or 8 positive inputs to the GATE (Expression 14)”        and are cumulative for the entire session regardless of        individual period status.

Counter 2 (Period Timer) maintains the time (Time High) for each300-second period with the end-of-period reset pulse coming from Counter3 (Period Counter).

Counter 3 (Period Counter) receives the trigger pulse output fromCounter 2 (Period Timer) and sends a trigger pulse and cumulative countvalue at the end of each 300-second period as determined by Counter 2(Period Timer).

Counter 4 receives the output from Threshold 8 (GATE STATUS) andmaintains a cumulative count of all events (Rising Edge) each time apositive (“1”) signal is received. This value is reset to “0” when anend-of-period trigger pulse is received from Counter 3 (Period Counter)which then provides a count of events for each period.

Counter 7 (FRWD Count) receives the signal from Expression 15 (FRWDFlight Record) into its “rising edge” element and sends a trigger pulseto Score 2 (FULL CONTROL TTL) once for every 3 inputs.

Counter 8 (LOFT) receives the output from Expression 36(VerticalDisplayControl) and sends the value representing the totalduration of all positive value signals to Expression 42 (LoftCalc).

Sample/Hold Object include:

Sample/Hold 1 receives the output from Counter 4 as well as theend-of-period trigger pulse from Counter 3 (Period Counter). At the endof each period it will maintain the former period's event total for 15seconds (Dwell) after the next period starts.

Sample/Hold 2 receives the trigger output from Counter 7 (FRWD Count)and a Hold command from Expression 35 and sends a trigger output toScore 1. The Hold command freezes the output during the 30-secondcalibrations.

Numeric Display Objects include:

Meter 1 (High Count) displays the output from Counter 1 (HIGH) as wholeintegers. There is a 35.0 ms refresh rate with no averaging.

Meter 2 (PERIOD #) displays the output from Expression 2 as wholeintegers. There is a 35.0 ms refresh rate with no averaging.

Meter 3 (Period Time) displays the output from Counter 2 (Period Timer)to one decimal place in standard time format. There is a 35.0 ms refreshrate with no averaging.

Meter 4 (Period Score) displays the output from Sample/Hold 1 as wholeintegers. There is a 35.0 ms refresh rate with no averaging.

Meter 10 (GATE EVENTS TTL) displays the output from Counter 1 (HIGH) aswhole integers on the Pilot Control Screen. There is a 35.0 ms refreshrate with no averaging.

Meter 12 (Flight Time) displays the output from Expression 37 (ReverseTimer) as a countdown timer on the Pilot Control Screen with no decimalsin standard time format. There is a 35.0 ms refresh rate with noaveraging.

Meter 14 (PCT Value) displays the output from PCT Calculation(Expression 8) as whole integers on the Pilot Control Screen. There is a35.0 ms refresh rate with 500 ms averaging.

Meter 15 (Flight #) displays the output from Expression 2 as wholeintegers on the Pilot Control Screen. There is a 35.0 ms refresh ratewith no averaging.

Bar Graph Display include:

Bar Graph 4 (Calibration) displays the first 30 seconds of each periodfrom Counter 2 (Period Timer) with no averaging and a 35.0 ms refreshrate. Control of the UFO/blimp is withheld during these 30 seconds.

Continuous Midi Generator includes Continuous MIDI 1 (Event Bell)provides a soft bell tone (MIDI Note Value=55; Volume=30) for each eventas defined by Threshold 8 (Gate Status).

Module VI

Module Six is responsible for calculating and maintaining an alternatingaudio/visual feedback display of the primary data streams as secondarypilot control. The outputs of Module Six are used by the pilot/traineeas baseline guides if subjective loss of control is experienced. Thismodule is comprised of a total of 10 objects: 4 equation objects, 2counter objects, 2 Boolean NOT gates and 2 video players.

Calculation Objects include:

Expression 10 (In1<6) receives the output of Counter 5 (SEP/DRIVE) andsends out a positive value (“1”) until the separation calculation hasmaintained its target value for a total of 6 seconds.

Expression 11 (In1>6) receives the output of Counter 6 (PCT/ASTROG) andsends out a negative value (“0”) until the PCT calculation has met itstarget value for a total of 6 seconds.

Expression 12 (If(In1=1), 1, 0) receives the output of Expression 10 andrelays the pass/fail data to NOT 2.

Expression 13 (If(In1=1), In2, 0) receives the output of Expression 10as In1 and Threshold 3 (FreqSep) as In2. This enables the audio/visualdisplays of Degree of Separation.

Counter Objects include:

Counter 5 (SEP/DRIVE) receives the pass/fail output of Threshold 3 (FreqSep) and accumulates the pass time which is sent to Expression 10. Thevalue is reset when the condition of Expression 11 is positive.

Counter 6 (PCT/ASTROG) receives the pass/fail data of Expression 9 andaccumulates the pass time which is sent to Expression 11. The value isreset when the condition of NOT 3 is negative.

Boolean Operators include:

NOT 2 receives the pass/fail data from Expression 12, reverses it andsends this signal as activation for the audio/visual displays of PCT.This has the effect of activating the PCT displays only when theFrequency Separation condition has been met.

NOT 3 receives the pass/fail data from Expression 10, reverses it andsends it to reset Counter 6. This has the effect of resetting the PCTdisplay counter when the Frequency Separation condition is met.

Video Players include:

Video Player 1 (Degree of Sep) receives signal output data fromThreshold 3 (FreqSep) and enable control data from Expression 13. Theproportional position of the video display responds to a range of −500 mto +500 m with averaging of 500 ms. The video file is “UFO Radar.avi”.

Video Player 2 (PCT) receives signal output data from Expression 8 (PCTCalculation) and enable control data from NOT 2. The proportionalposition of the video display responds to a range of 0 to 300 withaveraging of 500 ms. The video file is “UFO Radar.avi”.

Module VII

Module Seven is responsible for converting the neural net adjustedthreshold values from Module Two into a rational data stream that isfurther converted into PPM instructions to control the speed of thevertical propeller (or other primary control output) whilesimultaneously providing audio-visual feedback to the pilot/trainee.This is the primary learning algorithm and, in conjunction with thefeedback information from Modules Four, Five and Six, creates thelearning environment for enhanced cognitive functioning. The use of the“ratio of ratios” or “regression of regressions” equation in Expression18 combined with the adaptive quality of the neural nets allows thissoftware design to adjust itself to the training requirements of eachindividual. This module comprises 29 objects: 5 average calculators, 10equation objects and 14 display objects. The 14 display objects include:5 numeric readouts, 3 video players, 1 graphic trend display, 4 bargraph displays and 1 MIDI player.

Average Calculators include:

Average 1 (Alpha THRESH) receives output from the low threshold value ofThreshold 1 (Incr Alpha Coh) and creates a dynamic average across a120-second moving period.

Average 2 (Beta THRESH) receives output from the low threshold value ofThreshold 2 (Decr Beta Coh) and creates a dynamic average across a120-second moving period.

Average 3 (Alpha Thresh) receives output from the low threshold value ofThreshold 1 (Incr Alpha Coh) and creates a dynamic average across a5-second moving period.

Average 4 (Beta Thresh) receives output from the low threshold value ofThreshold 2 (Decr Beta Coh) and creates a dynamic average across a5-second moving period.

Average 5 receives output from Expression 17 (Ratio PPM) and creates adynamic average across a 1-second moving period.

The Calculation Objects include:

Expression 17 (Ratio to PPM) receives the output from Expression 29(High Value Compressor) and processes it through this logical statement:

CEIL(MAX(MIN(((9000−((In1−1)*8000))+8500),18000),9000))

SUBTRACT “1” from the current ratio value (In 1) and MULTIPLY the resultby the PPM constant (“8000”). ADD this resultant to “8500” THEN SUBTRACTthe new value from “9000”. IF this number is less than “18,000” ANDgreater than “9000” THEN truncate it to an integer and pass it into thedata stream ELSE IF number is greater than “18,000” THEN pass “18,000”into the data stream ELSE IF number is less than “9000” THEN pass “9000”into the data stream.

This expression converts the ratio data into the PPM language used bythe RF transmitter and restricts output to the range: 18,000 to 9000.The value of 18,000 is both the lower limit and the null signal, and thevalue of 9000 is the higher limit. Decreasing the PPM constant (thenumber after the asterisk) will extend the usable ratio range but willalso decrease vertical responsiveness. Usable ratio range: 0.95-2.07.

Expression 18 (A/B COH RATIOS) creates a dynamic ratio of ratios(regression of regressions) by dividing the ratio of the short periodmoving average of the threshold values of the alpha-theta and betathreshold objects by the ratio of the long period moving average of thethreshold values of the alpha-theta and beta threshold objects. To thisratio a threshold correction term from Expression 19 (ADJ THRESH MEANDIFF) is added (Vertical Boost). This value is multiplied by 100,truncated to integer form (CEIL) and returned to its original magnitude:

(CEIL(((In1/In2)/(In3/In4)+In5)*100)/100)

This value represents the RATIO OUTPUT which is used as the verticalcontrol data stream in this design. This data stream is sent toExpression 29 (High Value Compressor) before being incorporated into allcalculations for output control that follow.

Data input is received as follows:

-   -   In1=Average 3 (Alpha Thresh, 5.0 sec)    -   In2=Average 4 (Beta Thresh, 5.0 sec)    -   In3=Average 1 (Alpha THRESH, 120.0 sec)    -   In4=Average 2 (Beta THRESH, 120.0 sec)    -   In5=Expression 19 (ADJ THRESH MEAN DIFF)    -   In6=Expression 43 (LOW POWER ASSIST)

Expression 19 (ADJ THRESH MEAN DIFF) receives the outputs from Average 1(In1) and Average 2 (In2) and processes them through this logicalstatement:

MAX(((In1−In2)/3),0)

This expression creates a correction term that cannot drop below “0”that is sent to Expression 18 (A/B COH RATIOS) and Meter 13 (VERTICALBOOST) and provides a small proportional improvement for verticalresponsiveness when the alpha coherence thresholds are greater than thebeta coherence thresholds. Expression 21 (MIN((18000−In1), 8000))reverses the signal from Expression 22 (Module 5: Start Delay) and sendsit to the audio-visual elements. This re-reversal keeps the up and downorientation normalized since the PPM data decreases from 18,000 to 8500as the vertical lift increases.

Expression 29 (HIGH VALUE COMPRESSOR) modifies the data stream fromExpression 18 (A/B COH RATIOS) with the following logical statement:

If(In1>1.2,(((In1−1.2)*0.32)+1.2),In1)

IF flight altitude exceeds mid-level THEN correct all data beyond thatpoint to 32% of the input values. This statement has the effect ofreducing overall lift beyond mid-altitude to allow for a more stablerange of data by decreasing the degree of vertical movement in the uppervertical range. Increasing 1.2 will delay the EFFECT of the compression.Increasing 0.32 will diminish the DEGREE of compression. These data arethen sent to the following devices: Expression 16 (Forward Base Value),Expression 17 (Ratio to PPM), Expression 24 (ROTATION CALC), Expression28 (Vertical Control State), Expression 30 (PilotControlStatus),Expression 36 (VerticalDisplayControl), Expression 39(FlightMeterControl), Expression 43 (LOW POWER ASSIST), Meter 11(POWER), Meter 16 (POWER OUTPUT) and Trend 4 (FLIGHT RECORD).

Expression 38 (Blue Line) receives the output from Expression 30 andsends a continuous signal with a value of “1” to Ch3 of Trend 4 whichhas the effect of placing a blue line at “1.0” on the Flight Record.

Expression 39 (FlightMeterControl) receives the output from Expression29 and uses the following logical statement:

MIN(2,In1)

This has the effect of limiting the output of this Expression to no morethan “2”. This output goes to the Position input of Video Player 7(Flight Meter).

Expression 42 (Loft Calc) receives input from Counter 8 and Expression 2and uses the following logical statement:

MIN((In1/(Time−(In2*30))),1)

This calculates the running ratio of the amount of time the basic flightratio is above 0.99 against the total running time excluding thecalibration periods. This value is sent to Expression 44 (NN Control),Bar Graphs 7 & 8, and Meter 17.

Expression 43 (Low Power Assist) receives the outputs from Expression 44(NN Control), Expression 29 and Threshold 8 and processes these datathrough the logical statement:

If(((In1=0)&(In2=1)&(In3<1.15)),2.5,0)

IF LOFT % is low (<0.6) AND GATE STATUS is low (<7) AND POWER RATIO isbelow 1.15 THEN provide power assist of 2.5% to Expression 18 ELSE senda null value, “0”.

Expression 44 (NN Control) (Neural Net Control) receives the outputsignal from Expression 42 (Loft Calc). IF this signal's value dropsbelow 0.6, THEN a value of “1” is sent out to Input 1 of Expression 43(Low Power Assist) ELSE a null value, “0”, is sent.

Numeric Display Objects include:

Meter 9 (VERT PPM DATA) receives the output from Expression 22 (StartDelay) and reports in integer format the PPM data calculated inExpression 17 (Ratio PPM). Averaging is 500 ms with a 35 ms refreshrate.

Meter 11 (POWER) receives the output from Expression 29 (High ValueCompressor) and presents it on the Pilot Control Screen accurate tothree decimal places. There is a 35 ms refresh rate with no averaging.

Meter 13 (V BOOST) receives the output from Expression 19 (ADJ THRESHMEAN DIFF) and presents it accurate to four decimal places. There is a35 ms refresh rate with no averaging.

Meter 16 (POWER OUTPUT) receives the output from Expression 29 (HighValue Compressor) and presents it on the Instrument screen accurate tofour decimal places. There is a 35 ms refresh rate with no averaging.

Meter 17 (LOFT %) receives the output from Expression 29 (High ValueCompressor) and presents it on the Instrument screen accurate to fourdecimal places. There is a 35 ms refresh rate with no averaging.

The Video Players include:

Video Player 3 (ALTITUDE SIMULATOR) receives the output from Expression21 which controls a video representation of the relative verticalposition of the control object. The proportional position of the videoimage responds to an input range of 1 to 9,000 with no averaging. Thevideo file is “TubeRise.avi”.

Video Player 4 (VERTICAL PROP) receives the pass/fail output fromExpression 36 and presents a video representation of a propeller whichis synchronized to be activated when the vertical propeller of thecontrol object is activated. There is no averaging. The video file is“VertProp408.avi”.

Video Player 7 (Flight Meter) receives the output from Expression 39into its Position input and presents an image of a needle meter withspecial markings to assist data interpretation. The position andmovement of the needle is proportional to the fully processed ratiosignal. The input range is set to “0-1.9” with 1.0 second averaging. Thevideo file is “FlightMeterB.avi”.

Graphic Trend Display include:

Trend 4 (FLIGHT RECORD) receives the output from Expression 29 (HighValue Compressor), Expression 15 (FRWD Flight Record), Expression 35(Flight Start Record) and Expression 38 (Blue Line) and presents thegraphic output on the Pilot Control Screen as a history of vertical andforward flight activity for each 5-minute period. There are horizontaldivisions of 30 secs with a vertical axis display range of 0.5 to 2.5and 1.0 sec averaging.

Bar Graph Displays include:

Bar Graph 3 (LPA) receives the output from Expression 43 (Low PowerAssist) and acts as a signal indicator by changing color completely whenthe Low Power Assist conditions have been met. There is a 35 ms refreshrate and no averaging.

Bar Graph 6 (V Boost On) receives the output from Expression 19 and actsas a signal indicator by changing color completely when the AdjustedMean Threshold Difference conditions have been met. There is a 35 msrefresh rate and no averaging.

Bar Graph 7 (PREP) receives the output from Expression 42 (Loft Calc)and presents the data in the range of 0-0.50. There is no averaging anda 35 ms refresh rate.

Bar Graph 8 (OPTIMIZE) receives the output from Expression 42 (LoftCalc) and presents the data in the range of 0.50-1.0. There is a 35 msrefresh rate and no averaging.

MIDI Player include:

MIDI 1 (Lift Tone) receives the output from Expression 21 for note pitchvalue and Expression 36 as the enabling signal. Input range is 0 to8,000 and MIDI note range is 40 to 100. The notes are set to follow apentatonic major “A” scale at a volume of 40% with no averaging. TheMIDI player is set to produce no sound until activated.

Module VIII

Module Eight receives the output of BandRatio 1 (Module Two) and createsa triple state directional control output that can be proportional orstate dependent. This parameter is the secondary pilot control. Thismodule comprises of 8 objects which include 4 expression calculations, 1threshold calculator and 3 display objects.

Threshold Calculator includes:

Threshold 9 (Rotation Index: A/T Ratio) receives the output fromBandRatio 1 (Module Two) and sets upper and lower limits at a basetolerance of 90% success and 1 sec averaging. The auto-thresholdfunction is controlled by the pass/fail output from Threshold 8.

Calculation Objects include:

Expression 20 (RIGHT Rotation Threshold) receives both the signal outputand the high threshold value from Threshold 9 (Rotation Index: A/TRatio). IF the signal (In1) is greater than the threshold (In2) THEN thevalue “15,000” is sent to Expression 24 (ROTATION CALC) to initiate aright (clockwise) spin. IF the signal is less than the threshold THEN anull value is sent.

Expression 23 (LEFT Rotation Threshold) receives both the signal outputand the low threshold value from Threshold 9 (Rotation Index: A/TRatio). IF the signal (In1) is less than the threshold (In2) THEN thevalue “20,000” is sent to Expression 24 (ROTATION CALC) to initiate aleft (counter-clockwise) spin. IF the signal is greater than thethreshold THEN a null value is sent.

Expression 24 (ROTATION CALC) receives outputs from Expression 29 (HighValue Compressor) (In3), Expression 20 (RIGHT Rotation Threshold) (In1),Expression 23 (LEFT Rotation Threshold) (In2) and Counter 2 (PeriodTimer) (In4) and calculates the AMPLITUDE BALANCE OUTPUT by processingthese data through the following logical statement:

If(((In1+In2)=0)|(In3<1.2)|(In4<31),18000,(In1+In2))

IF both rotation values are null OR IF the altitude is too low OR IF thetimer is in the 30-second calibration phase THEN a value of “18,000” issent to In1 of Server 1 (LINK to RF TRANS) which will arrest rotationalaction ELSE the sum of In1+In2 will send appropriate rotationalinstructions.

Expression 40 (Spin Meter Control) receives output from Expression 30(PilotControlStatus) and instructs Video Player 6 to engage the Spinvideo only when spin is possible.

If((In1>29),In1,0)

Display Objects include:

Bar Graph 5 (Stabilization) receives data from the Pass % output ofThreshold 5 (A-T Ratio) and presents information within a range of0.46-0.51 with 10 second averaging and a 35-ms refresh rate.

Meter 8 (Stabilization) receives data from the Pass % output ofThreshold 5 (A-T Ratio) and presents numeric information to 3 decimalplaces with 10 second averaging and a 35-ms refresh rate.

Video Player 6 (SPIN) receives Position data from the Ratio output ofThreshold 9 and Enable instructions from Expression 40. The video fileis “UFO Spin E5f.avi”, and there is 2-second averaging.

Module IX

Module 9 is responsible for pilot control status displays, control offorward thrust as the third control parameter and connection to theServer object which provides the data output link to the PPM softwarewhich will ultimately be controlling the radio transmitter. This modulecomprises of 22 objects: 13 equation calculations, 1 thresholdcalculator, 1 numeric display, 3 video players, 2 score devices, 1 audioplayer and 1 server object.

The Calculation Objects include:

Expression 15 (FRWD Flight Record) receives input from Expression 30(PILOT CONTROL STATUS) and determines when forward flight has beenactivated.

If((In1=56),0.75,0)

IF forward flight has been approved by Expression 30 (PILOT CONTROLSTATUS) THEN direct Channel 2 of Trend 4 (FLIGHT RECORD) to place asmall yellow bar on the Flight Record screen.

Expression 16 (FORWARD Base Value) calculates the DIRECT PCT OUTPUTwhich is used as forward thrust in this design and receives the outputsfrom 8 sources:

-   -   In1=low threshold value of Threshold 1 (Incr Alpha Coh)    -   In2=low threshold value of Threshold 2 (Decr Beta Coh)    -   In3=low threshold value of Threshold 6 (SMR COH)    -   In4=low threshold value of Threshold 3 (Freq Sep)    -   In5=low threshold value of Threshold 7 (GAMMA COH)    -   In6=count output of Counter 2 (Period Timer)    -   In7=output value of Expression 29 (High Value Compressor)    -   In8=pass/fail value of Threshold 10 (Thrust Control)    -   In9=pass/fail value of Threshold 9 (Rotation Index)

These data are processed according to the following logical statement:

If((In6>30)&(In8=1)&(In7>1.2)&(In9=1),(MAX(CEIL((In1−In2+In3+In4+(In5*2))*4200),0)),0)

IF the period timer is past 30 seconds AND IF the PCT value is abovethreshold AND IF the altitude is above minimum safe height AND IF therotation index indicates there is no spin THEN send the integer value ofthe sum of the low threshold values of Thresholds 1, 2 (Threshold 2 issubtracted because it varies inversely relative to the other values.),3, 6 & 7 (Threshold 7 is doubled to enhance weighting of the PCT value.)multiplied by 4200 (initial PPM conversion factor) to Expression 25(Speed Governor) ELSE IF this value is negative THEN send a null value.

Expression 25 (Speed Governor) receives the output from Expression 16(FORWARD Base Value) and sets an upper limit on the PPM value forforward thrust:

MIN(In1,7500)

The lower of the two values will be sent to Expression 31 (THRUSTSTATUS), Expression 33 (TEST FORWARD THRUST) and Meter 14 (THRUST) withforward thrust responding inversely to the value of Expression 16(FORWARD Base Value).

Expression 26 (Speed to PPM) receives the output from Expression 33(TEST FORWARD THRUST) and converts it to the usable range for the PPMsoftware by adding a value of 12,500 then sends this value to Server 1(In3).

Expression 27 (Basic Control State) (If((In1>30), In2, 1)) receives theoutput from Counter 2 (Period Timer) (In1) and Expression 28 (FlightControl State) (In2) and provides the flight status control data. IF theperiod is past the 30-second adjustment delay THEN the value of In2 issent to Expression 30 (PILOT CONTROL STATUS) ELSE a value of “1” issent.

Expression 28 (Flight Control State) (If((In1>1.2), 10, 5)) receives theoutput from Expression 29 (High Value Compressor) and sends it toExpression 27 (Basic Control State). IF the altitude is above theminimum safe value THEN send a value of “10” ELSE send a value of “5”.

Expression 30 (PILOT CONTROL STATUS) receives the output from Expression27 (Basic Control State) (In1), Expression 31 (Thrust Status) (In2) andExpression 24 (ROTATION CALC) (In3) and converts those data intoinstructions for Video Player 5 (PilotControlStatus) using the followinglogical statement:

If((In1=10),(In2+(In3/500)),In1)

IF the Basic Control State (In1) indicates flight altitude is above safeminimum THEN send control instruction data to PilotControlStatus ELSEsend the value of In1.

Pilot Control Status Calculations (Output = Video Frame #) OUTPUT: 1 530 36 40 56 SOURCE: Exp27 Exp28 Exp24 Exp24 Exp24 Exp24 + 31 CALC: In1In1 In3/500 In3/500 In3/500 In2 + (In3/500) VERTICAL: OFF ON ON ON ON ONSPIN: OFF OFF RIGHT NO LEFT NO SPIN SPIN THRUST: OFF OFF OFF OFF OFF ON

Expression 31 (THRUST STATUS) receives input from Expression 25 (SpeedGovernor) and Threshold 9 (Rotation Index) and sends thrust permissionstatus to Expression 30 (PILOT CONTROL STATUS).

If((In1<1000)|(In2=0),0,20)

IF Thrust is too low OR UFO is spinning THEN 0=No Thrust Allowed ELSE20=Thrust On.

Expression 32 (NO SPIN Tone) receives input from Expression 24 (RotationCalc). When there is no rotational signal, send a positive value (“1”)to Audio Player 2 (No Spin).

Expression 33 (TEST FORWARD THRUST) receives input from Expression 25(Speed Governor) and Counter 2 (Period Timer) and signals a maximallateral rotor burst to initialize the Forward Thrust chip.

If(((In2>13)&(In2<18)),9500,In1)

IF the period timer is between 13 AND 18 seconds THEN send a maximalburst signal to Expression 26 (Speed to PPM) ELSE pass through forwardthrust data.

Expression 34 (LATERAL ROTOR TEST ALERT) receives input from Expression30 (Pilot Control Status) and Counter 2 (Period Timer) and controls thevideo alert for a maximal lateral rotor burst to initialize the ForwardThrust chip controlled by Expression 33 (TEST FROWARD THRUST).

If((In2>12)&(In2<20),70,In1)

IF the period timer is between 12 AND 20 seconds THEN send video alertdata to Video Player 5 (PilotControlStatus) (Frame #70) ELSE passthrough data from Expression 30.

Expression 35 (Flight Start Record) receives input from Counter 2(Period Timer) and signals Trend 4 (FLIGHT RECORD) to place a verticalred bar at the start of each flight record.

Expression 41 (EEG Status Control) receives data from Expression 35 anddirects the video file, BL.avi, to display either frame #1 or frame #8.

If((In1=4),1,8)

The Threshold Calculator includes:

Threshold 10 (Thrust Control) receives the output from Expression 8 (PCTCalculation) with a base tolerance of 60% success and 250 ms averaging.Auto-threshold control is from the pass/fail output of Threshold 9(Rotation Index: A/T Ratio). When the pass condition is met, a value of“1” is sent to In8 of Expression 16 (FORWARD Base Value).

The Score Devices include:

Score 1 (F CTRL/FLT) receives output from Sample/Hold 2 and displays arunning total for each period of full control events as calculated byCounter 7 (FRWD Count). Reset instructions are received from NOT 4.Sound output is not enabled.

Score 2 (FULL CONTROL TTL) receives data from the trigger output ofCounter 7 (FRWD Count) and displays the running total of full controlevents throughout the entire session. Sound output is enabled so that abell tone is heard with each event. The sound file is “ding.wav”.

The Audio Player includes:

Audio Player 2 (No Spin) receives input from Expression 32 (NO SPINTone) into the Trigger input which activates the audio file, “UFOSteady.wav”. Volume is set to 30, and both the “repeat” and “restart ontrigger” functions are selected.

The Video Players include:

Video Player 5 (PilotControlStatus) receives the output from Expression34 and presents the status messages for Vertical, Spin, and Thrustcontrols as calculated in Expression 30 as well as the alert message forthe Rotor Test during the calibration period. The video file is“ControlStatus3d.avi”. Input directs the video object to the specificframe in the video file containing the appropriate message with noaveraging.

Video Player 8 (Forward Thrust) receives the output from Expression 25(Speed Governor) and presents a video representation that providesproportional feedback relative to the values calculated in Expression16. The video file is “Thrust.avi”. Input directs the video object torespond within the range of 0-6K with 0.5 sec averaging.

Video Player 9 (EEG Status) receives the output from Expression 41 (EEGStatus Control) and presents the status message for the EEG link. Thevideo file is “BL.avi”. Input directs the video object to the specificframe in the video file containing the appropriate message with noaveraging.

The Server Object is described as follows:

Server 1 (LINK to RF TRANS) receives three outputs as follows:

In1=AMPLITUDE BALANCE OUTPUT (Rotation): Source=Expression 24 (ROTATIONCALC) In2=RATIO OUTPUT (Vertical): Source=Expression 22 (Start Delay)

In3=DIRECT PCT OUTPUT (Forward thrust): Source=Expression 26 (Speed toPPM)These data streams are made available to a PPM conversion program:SC8000MFC4.exe which has been modified specifically to receive data fromthe BioExplorer Server object. These data are transmitted through I/OCOM4 which is activated by running: HidCommInst.exe. These are thecontrolling data for the external objects, devices or displays.

Module X contains the neural net and auto-logic cascades.

Module 10 (or “X”) permits this software design to adapt to individualusers. The two neural nets are: 1) Primary Threshold Control forprocessing data input and 2) Pilot Threshold Control Assist forprocessing data output.

The Primary Threshold Control Cascade includes:

Step 1 at OR 2 (NN ON) which controls Step 2 as follows: IF coherencefrequency dispersion is low (NOT 1) OR IF Low Power Assist is engaged(Expression 43) THEN allow Alpha-theta Coherence (Threshold 1) to adjustthreshold value.

Step 2 at Threshold 1 (Incr Alpha Coh) which controls Step 3 as follows:IF Alpha-theta Coherence is doing well (above threshold) THEN allowFrequency Separation (Threshold 3) to adjust threshold value.

Step 3 at Threshold 3 (Freq Sep) which controls Steps 4a & 4b asfollows: IF Frequency Separation is doing well (above threshold) THENallow BOTH Alpha Synchrony (Threshold 4) AND Alpha/Theta Ratio(Threshold 5) to adjust limits.

Step 4 is Alpha-Theta Amplitude Controls which includes:

Step 4a, Threshold 4 (Alpha Sync), which controls Step 5a as follows: IFAlpha Synchrony is doing well (within threshold limits) THEN allow BetaCoherence (Threshold 2) to adjust threshold value; and

Step 4b, Threshold 5 (A-T Ratio), which controls Step 5b as follows: IFAlpha/Theta Ratio is doing well (within threshold limits) THEN allow SMRCoherence (Threshold 6) to adjust threshold value.

Step 5 is Beta Coherence Controls which includes:

Step 5a, Threshold 2 (Decr Beta Coh), and Step 5b, Threshold 6 (SMRCOH).

Step 6 is at Expression 9 which controls Step 7 as follows: IF combinedthreshold outputs (PCT data stream, Expression 8) exceed minimum valueTHEN allow GAMMA COH (Threshold 7) to adjust threshold value.

Step 7, Threshold 7 (GAMMA COH), is the outermost point on the inputauto-logic cascade.

The Pilot Threshold Control Assist includes:

Step P1 at Threshold 8 (GATE STATUS) which controls Step P2 as follows:IF PCT Gate is open THEN allow Rotation Index (Threshold 9) to adjustlimits.

Step P2 at Threshold 9 (Rotation Index A/T Ratio) which controls Step P3as follows: IF Rotation Control is hovering near center (no rotation)THEN allow Thrust Control (Threshold 10) to adjust threshold values.

Step P3 at Threshold 10 (Thrust Control) which is the outermost point onthe output auto-logic cascade.

Those of skill in the art would understand that information and signalsmay be represented using any of a variety of different technologies andtechniques. For example, data, instructions, commands, information,signals, bits, symbols, and chips that may be referenced throughout theabove description may be represented by voltages, currents,electromagnetic waves, magnetic fields or particles, optical fields orparticles, or any combination thereof.

Those of skill would further appreciate that the various illustrativelogical blocks, modules, circuits, and algorithm steps described inconnection with the embodiments disclosed herein may be implemented aselectronic hardware, computer software, or combinations of both. Toclearly illustrate this interchangeability of hardware and software,various illustrative components, blocks, modules, circuits, and stepshave been described above generally in terms of their functionality.Whether such functionality is implemented as hardware or softwaredepends upon the particular application and design constraints imposedon the overall system. Skilled artisans may implement the describedfunctionality in varying ways for each particular application, but suchimplementation decisions should not be interpreted as causing adeparture from the scope of the present invention.

The various illustrative logical blocks, modules, and circuits describedin connection with the embodiments disclosed herein may be implementedor performed with a general purpose processor, a Digital SignalProcessor (DSP), an Application Specific Integrated Circuit (ASIC), aField Programmable Gate Array (FPGA) or other programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof designed to perform the functions described herein.A general purpose processor may be a microprocessor, but in thealternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

The steps of a method or algorithm described in connection with theembodiments disclosed herein may be embodied directly in hardware, in asoftware module executed by a processor, or in a combination of the two.A software module may reside in Random Access Memory (RAM), flashmemory, Read Only Memory (ROM), Electrically Programmable ROM (EPROM),Electrically Erasable Programmable ROM (EEPROM), registers, hard disk, aremovable disk, a CD-ROM, or any other form of storage medium known inthe art. An exemplary storage medium is coupled to the processor suchthe processor can read information from, and write information to, thestorage medium. In the alternative, the storage medium may be integralto the processor. The processor and the storage medium may reside in anASIC.

The previous description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedherein may be applied to other embodiments without departing from thespirit or scope of the invention. Thus, the present invention is notintended to be limited to the embodiments shown herein but is to beaccorded the widest scope consistent with the principles and novelfeatures disclosed herein.

1. A method of training, comprising the steps of: obtaining at least afirst brainwave signal and a second brainwave signal from a patient;filtering the first brainwave signal into at least a first frequencyband and filtering the second brainwave signal into at least a secondfrequency band; determining a coherence between the filtered firstbrainwave signal and the filtered second brainwave signal; comparing thedetermined coherence between the filtered first brainwave signal and thefiltered to second brainwave signal to a coherence threshold; andgenerating at least a first control signal based on the comparison ofthe coherence to the coherence threshold; and controlling at least onecontrol object using at least one control signal based on thecomparison, whereby the control object moves and provides visualfeedback to the patient tending to cause the user to produce desiredbrainwaves.
 2. The method of claim 1, further comprising placing aplurality of sensors around a skull of a patient wherein the sensorsfacilitate obtaining the first brainwave signal and the second brainwavesignal.
 3. The method of claim 1, wherein the step of filtering thefirst brainwave into at least the first frequency brand and filteringthe second brainwave signal into at least the second frequency bandcomprises filtering the first brainwave into a plurality of frequencybands and filtering the second brainwave signal into a correspondingplurality of frequency bands.
 4. The method of claim 3, wherein the stepof filtering comprises filtering the first brainwave and the secondbrainwave into at least 4 frequency bands consisting of substantiallythe alpha theta frequency range, substantially the sensory motor rhythmfrequency range, substantially the beta frequency range, andsubstantially the gamma frequency range.
 5. The method of claim 1,wherein the step of filtering comprises performing a Fourier transformon the first brainwave signal and the second brainwave signal.
 6. Themethod of claim 1, further comprising the step of determining a firstamplitude and a second amplitude, the first amplitude corresponding tothe amplitude of the first brainwave and the second amplitudecorresponding the second brainwave.
 7. The method of claim 1, whereinthe first frequency band and the second frequency band are the same. 8.The method of claim 3, wherein the plurality of first frequency bandsand the plurality of second frequency bands comprise the ranges of about4 Hz to about 12 Hz and about 12 Hz to about 30 Hz.
 9. The method ofclaim 8, wherein the plurality of first frequency bands and theplurality of second frequency bands comprise the ranges of about 6 Hz toabout 11 Hz and about 12 Hz to about 28 Hz.
 10. The method of claim 1,wherein the step of controlling at least one control object comprisesthe step of causing a real object to move.
 11. The method of claim 1,wherein the step of controlling at least one control object comprisesthe step of causing a virtual object to move.
 12. The method of claim 6wherein the step of generating at least a first control signal furthercomprises generating at least a second control signal based on comparinga ratio of amplitudes to an amplitude threshold.
 13. The method of claim12 wherein the first control signal comprises a vertical control signaland the second control signal comprises a rotational control signal. 14.A method of converting brainwave signals into stable control signals forreal or virtual objects, the method comprising the steps of: receiving abrainwave signal; converting the received brainwave signal into a formatsuitable for signal processing; determining a coherence between thebrainwave signal and another brainwave signal; comparing the coherenceto a predetermined coherence threshold; outputting a first controlsignal based on the comparison of the coherence to the predeterminedcoherence threshold; controlling a real object based on the firstcontrol signal; determining whether the coherence meets thepredetermined threshold satisfactorily; and adjusting the predeterminedthreshold based on the determination of whether the coherence meets thepredetermined threshold satisfactorily.
 15. The method of claim 14,wherein the step of comparing the coherence to a predetermined coherencethreshold comprises a first predetermined coherence threshold and asecond predetermined threshold such that the comparison is successfulbased on obtaining a first predetermined threshold and unsuccessfulbased on not obtaining a second predetermined threshold such that thethreshold is satisfactorily met based on a hysteresis.
 16. The method ofclaim 14, wherein the thresholds compare a first coherence to aproceeding coherence to determine whether the coherence is increasing ordecreasing.
 17. The method of claim 14 further comprising the steps of:calculating a ratio comparing an amplitude of a portion of the brainwavesignal to an amplitude of another signal; comparing the ratio to apredetermined ratio threshold; outputting a second control signal basedon the comparison of the ratio to the predetermined ratio threshold; andwherein the step of controlling a real object is also based on thesecond control signal.
 18. The method of claim 14 further comprisingwirelessly transmitting the first control signal to the real object thatis located remotely.
 19. The method of claim 14 wherein another signalis a second brainwave signal.
 20. The method of claim 19 wherein: thestep of determining a coherence between the brainwave signal and anothersignal comprises determining a plurality of coherences between thebrainwave signal and the second brainwave signal; the step of comparingthe coherence to a predetermined coherence threshold comprises comparingthe plurality of coherences to a plurality of thresholds; and the stepsof determining whether the coherence meets the predetermined thresholdsatisfactorily; and adjusting the predetermined threshold based on thedetermination of whether the coherence meets the predetermined thresholdsatisfactorily further comprise the steps of: determining whether one ofthe plurality of coherences meets a corresponding one of the pluralityof predetermined thresholds satisfactorily and adjusting the one of thepredetermined thresholds until it is determined that the one of theplurality of coherences meets the corresponding one of the plurality ofpredetermined thresholds satisfactorily, and once the corresponding oneof the plurality of thresholds has been adjusted determining whether anext one of the plurality of coherences meets a corresponding next oneof the plurality of thresholds and adjusting the corresponding next oneof the plurality of thresholds until it is determined that the next oneof the plurality of coherences meets the corresponding next one of theplurality of thresholds satisfactorily, and repeating until all theplurality of coherences and the plurality of corresponding thresholdshave been adjusted.
 21. The method of claim 14 wherein the step ofoutputting the first control signal comprises contriving the firstcontrol signal into a wireless format and wirelessly transmitting thesignal.
 22. An apparatus for providing neurological feedback, theapparatus comprising: a filter adapted to receive a plurality of inputscorresponding to a plurality of brainwave signals of a user and toseparate each of the plurality of brainwave signals into a plurality offrequency bands; a coherence generator to receive each of the pluralityof brainwave signals filtered into the plurality of frequency bands andto generate at least one coherence between corresponding frequency bandsof the plurality of brainwave signals; a threshold module to receive theat least one coherence and to generate at least one signal when the atleast one coherence satisfies at least one predetermined coherencethreshold; a control module to receive the at least one signal when theat least one coherence satisfies the at least one predeterminedcoherence threshold and adapted to transmit at least one control signal,wherein the at least one control signal is adapted to be received by acontrol object that causes corresponding movement of a control object.23. The apparatus of claim 22, comprising a fuzzy logic feedback moduleto monitor the signal and adjust the at least one predeterminedcoherence threshold based on the signal satisfactorily meeting the atleast one predetermined coherence threshold.
 24. The apparatus of claim22, comprising an amplitude generator to generate a signal correspondingto an amplitude of at least one of the plurality of brainwave signalswherein the threshold module receives the signal corresponding to theamplitude of at leas to one of the plurality of brainwave signals andgenerates at least another signal when the amplitude satisfies at leastone predetermined amplitude threshold and wherein the control modulereceives the at least another signal and is adapted to transmit at leastanother control signal, wherein the at least another control signal isadapted to be received by the control object that causes correspondingmovement of a control object.
 25. The apparatus of claim 22 comprising aradio transmitter adapted to transmit the at least one control signal tothe control object.
 26. The apparatus of claim 22 wherein the controlobject is at least one of a real or virtual object.
 27. An apparatus forgenerating signals to control an object using a plurality of brainwavesfrom a user of the apparatus, the apparatus comprising: means forreceiving a plurality of brainwave signals; means for separating each ofthe plurality of brainwave signals into at least one frequency range;means for generating a coherence by comparing at least one of thebrainwave signals separated into at least one frequency range to atleast one other signal; and means for generating at least one objectcontrol signal when the generated coherence satisfies at least onepredefined coherence threshold.
 28. The apparatus of claim 27 comprisingmeans for generating an amplitude from at least one of the plurality ofbrainwave signals and means for generating at least another objectcontrol signal when the generated amplitude satisfies at least onepredefined amplitude threshold.
 29. The apparatus of claim 27 comprisingmeans for adjusting the at least one predefined coherence threshold.